Introduction

Our research focuses on performing Modelling on Google Play Store apps to uncover patterns, trends, and insights regarding app characteristics, user behavior, and installation patterns. We are trying to see how app popularity, defined as the number of installs, is impacted by the top five categories, last updated, app sizes, version, and other factors.

Smart Question

“Our research focuses on performing Modelling on Google Play Store apps to uncover patterns, trends, and insights regarding app characteristics, user behavior, and installation patterns. We are trying to see how app popularity, defined as the number of installs, is impacted by the top five categories, last updated, app sizes, version, and other factors.”

Specific: The question focuses on identifying how app popularity (defined by the number of installs) is influenced by well-defined variables, including the top five app categories, last update date, app size, version, and additional characteristics such as content rating, pricing model, and user reviews. It also aims to uncover specific patterns and trends in user behavior and installation patterns.

Measurable: The impact of each variable (categories, last update, size, version) on app popularity is quantifiable using metrics such as the number of installs, user reviews, ratings, app size in MB, frequency of updates, and category-specific rankings. This ensures that results can be expressed numerically or statistically.

Achievable: Given the availability of historical data from the Google Play Store (e.g., datasets spanning years and including app attributes), the analysis is feasible using data analysis techniques, statistical modeling, and machine learning. Open-source libraries and tools can efficiently handle the data preprocessing and modeling.

Relevant: The research is pertinent to app developers, marketers, and stakeholders in the mobile app ecosystem. Understanding the factors driving app installs directly addresses key industry challenges, such as improving app visibility, optimizing user engagement, and tailoring marketing strategies for success.

Time-specific: The research will use data from a specific timeframe (e.g., 2010-2018), ensuring that insights are grounded in a defined historical context. The results could also include temporal trends to observe how factors influencing popularity have evolved over time.

Overview

This research aims to analyze Google Play Store apps to uncover patterns, trends, and insights into how app characteristics influence popularity, defined by the number of installs. The study will involve systematic steps, including data cleaning, exploratory data analysis (EDA), modeling, and evaluation, to address the SMART research questions.

Data Preparation and Cleaning

Here, we have loaded the dataset ‘Google Play Store Apps’ stored in csv file using ()

#Loading the Dataset
data_apps <- data.frame(read.csv("googleplaystore.csv"))

#Checking the structure of the data
str(data_apps)

## 'data.frame':    10841 obs. of  13 variables:
##  $ App           : chr  "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
##  $ Category      : chr  "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" ...
##  $ Rating        : num  4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
##  $ Reviews       : chr  "159" "967" "87510" "215644" ...
##  $ Size          : chr  "19M" "14M" "8.7M" "25M" ...
##  $ Installs      : chr  "10,000+" "500,000+" "5,000,000+" "50,000,000+" ...
##  $ Type          : chr  "Free" "Free" "Free" "Free" ...
##  $ Price         : chr  "0" "0" "0" "0" ...
##  $ Content.Rating: chr  "Everyone" "Everyone" "Everyone" "Teen" ...
##  $ Genres        : chr  "Art & Design" "Art & Design;Pretend Play" "Art & Design" "Art & Design" ...
##  $ Last.Updated  : chr  "January 7, 2018" "January 15, 2018" "August 1, 2018" "June 8, 2018" ...
##  $ Current.Ver   : chr  "1.0.0" "2.0.0" "1.2.4" "Varies with device" ...
##  $ Android.Ver   : chr  "4.0.3 and up" "4.0.3 and up" "4.0.3 and up" "4.2 and up" ...

#First 5 rows of the dataset
head(data_apps)

##                                                  App       Category Rating
## 1     Photo Editor & Candy Camera & Grid & ScrapBook ART_AND_DESIGN    4.1
## 2                                Coloring book moana ART_AND_DESIGN    3.9
## 3 U Launcher Lite – FREE Live Cool Themes, Hide Apps ART_AND_DESIGN    4.7
## 4                              Sketch - Draw & Paint ART_AND_DESIGN    4.5
## 5              Pixel Draw - Number Art Coloring Book ART_AND_DESIGN    4.3
## 6                         Paper flowers instructions ART_AND_DESIGN    4.4
##   Reviews Size    Installs Type Price Content.Rating                    Genres
## 1     159  19M     10,000+ Free     0       Everyone              Art & Design
## 2     967  14M    500,000+ Free     0       Everyone Art & Design;Pretend Play
## 3   87510 8.7M  5,000,000+ Free     0       Everyone              Art & Design
## 4  215644  25M 50,000,000+ Free     0           Teen              Art & Design
## 5     967 2.8M    100,000+ Free     0       Everyone   Art & Design;Creativity
## 6     167 5.6M     50,000+ Free     0       Everyone              Art & Design
##       Last.Updated        Current.Ver  Android.Ver
## 1  January 7, 2018              1.0.0 4.0.3 and up
## 2 January 15, 2018              2.0.0 4.0.3 and up
## 3   August 1, 2018              1.2.4 4.0.3 and up
## 4     June 8, 2018 Varies with device   4.2 and up
## 5    June 20, 2018                1.1   4.4 and up
## 6   March 26, 2017                1.0   2.3 and up

Description of the App Dataset Columns

App: The name of the application, represented as a character string.
Category: The main category of the app, such as “ART_AND_DESIGN,” represented as a character string.
Rating: The average user rating of the app, recorded as a numeric value.
Reviews: The total number of user reviews for the app, shown as a character string.
Size: The size of the application, represented as a character string.
Installs: The approximate number of installations for the app, stored as a character string.
Type: Indicates whether the app is free or paid, represented as a character string.
Price: The price of the app, stored as a character string. Free apps are listed as “0,” while paid apps have a dollar amount.
Content.Rating: The target age group for the app, represented as a character string.
Genres: The genre(s) of the app.
Last.Updated: The date of the app’s last update, stored as a character string.
Current.Ver: The current version of the app, represented as a character string.
Android.Ver: The minimum Android version required to run the app, stored as a character string.

Apps

# Checking the type of the App 
typeof(data_apps$App)

## [1] "character"

Checking for duplicated apps and removing

#Display all the duplicated Apps
duplicate_apps <- aggregate(App ~ ., data = data_apps, FUN = length)  
duplicate_apps <- duplicate_apps[duplicate_apps$App > 1, ] 
duplicate_apps <- duplicate_apps[order(-duplicate_apps$App), ] 

#View(duplicate_apps)
#print(duplicate_apps)

print(paste("Number of duplicated Apps:",nrow(duplicate_apps)))

## [1] "Number of duplicated Apps: 404"

#Removing Na values and duplicates
data_clean <- data_apps[!is.na(data_apps$App), ] 
data_clean <- data_clean[!duplicated(data_clean$App), ] 

#(After removing the duplicates) Unique values
unique_apps <- length(unique(data_clean$App))
print(paste("Number of unique apps after removing the duplicates:", unique_apps))

## [1] "Number of unique apps after removing the duplicates: 9660"

Duplicate App Analysis:

404 apps were repeated either twice or thrice.
After removing duplicates, the dataset now contains 9660 unique apps.
Total duplicates removed: 1181 apps.

After dropping duplicate

str(data_clean$App)

##  chr [1:9660] "Photo Editor & Candy Camera & Grid & ScrapBook" ...

Price

typeof(data_apps$Price)

## [1] "character"

Convertion of Price to numerical

There is ‘$’ present after each price of the App. Check and remove before conversion.

#To check if there is dollar symbol present 
#data_clean$Price[]

# Remove dollar symbols and convert to numeric
data_clean$Price <- as.numeric(gsub("\\$", "", data_clean$Price))

#Recheck for dollar symbol
#data_clean$Price[]

All the dollar symbols are removed succesfully.

# Summary statistics for price
summary(data_clean$Price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   0.000   0.000   1.099   0.000 400.000       1

From the unique_df, there is a missing value present in the Price column. Let’s handle it!

Checking for missing values in Price

missing_na <- is.na(data_clean$Price)    
missing_blank <- data_clean$Price == "" 

sum(missing_na)

## [1] 1

sum(missing_blank, na.rm = TRUE)

## [1] 0

# Remove row where Price is NA or blank
data_clean <- data_clean[!is.na(data_clean$Price) & data_clean$Price != "", ]

Have removed one row #10473 which app does not have a category nameas it is not relevant to our analysis.

#Recheck for missing values
summary(data_clean$Price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   0.000   0.000   1.099   0.000 400.000

Missing values removed succesfully. (Price)

Type

#Checking the type of Type variable
table(data_clean$Type)

## 
## Free Paid 
## 8902  756

From the price column, we can see 8903 apps are free but it is misread somewhere in the Type column. So lets check!

#Checking for Missing values
print(paste("Missing values:",sum(is.na(data_clean$Type))))

## [1] "Missing values: 0"

data_clean[is.na(data_clean$Type), ]

##  [1] App            Category       Rating         Reviews        Size          
##  [6] Installs       Type           Price          Content.Rating Genres        
## [11] Last.Updated   Current.Ver    Android.Ver   
## <0 rows> (or 0-length row.names)

# Replace NaN or missing values in the Type column with "Free"
data_clean$Type[is.na(data_clean$Type)] <- "Free"

There is one row 9150, has a missing value for Type. As the price is 0, replaced it with “Free”.

Missing values handles succesfully. (Type)

Size

# Checking the type of the Size 
typeof(data_apps$Size)

## [1] "character"

Replacing Misiing values with the mean (Size)

# Replace "Varies with Device" in the Size column with NA
data_clean$Size[data_clean$Size == "Varies with device"] <- NA
data_clean <- data_clean[!grepl("\\+", data_clean$Size), ]
data_clean$Size <- ifelse(grepl("k", data_clean$Size),
                          as.numeric(gsub("k", "", data_clean$Size)) *
0.001,  # Convert "K" to MB
                          as.numeric(gsub("M", "", data_clean$Size)))
# Remove "M" for megabytes
# Calculate and display the mean size for each category in the 'Type' column
mean_size_by_type <- tapply(data_clean$Size, data_clean$Category,
mean, na.rm = TRUE)
print(mean_size_by_type)

##      ART_AND_DESIGN   AUTO_AND_VEHICLES              BEAUTY BOOKS_AND_REFERENCE 
##           12.370968           20.037147           13.795745           13.134701 
##            BUSINESS              COMICS       COMMUNICATION              DATING 
##           13.867194           13.794959           11.307430           15.661119 
##           EDUCATION       ENTERTAINMENT              EVENTS              FAMILY 
##           19.057101           23.043750           13.963754           27.187988 
##             FINANCE      FOOD_AND_DRINK                GAME  HEALTH_AND_FITNESS 
##           17.368127           20.494318           41.866609           20.669707 
##      HOUSE_AND_HOME  LIBRARIES_AND_DEMO           LIFESTYLE MAPS_AND_NAVIGATION 
##           15.970258           10.602883           14.844916           16.368121 
##             MEDICAL  NEWS_AND_MAGAZINES           PARENTING     PERSONALIZATION 
##           19.189399           12.470189           22.512963           11.224624 
##         PHOTOGRAPHY        PRODUCTIVITY            SHOPPING              SOCIAL 
##           15.666158           12.342505           15.491435           15.984090 
##              SPORTS               TOOLS    TRAVEL_AND_LOCAL       VIDEO_PLAYERS 
##           24.058361            8.782837           24.204410           15.792756 
##             WEATHER 
##           12.680036

# Loop through each row and replace NA values in the Size column with the mean size of the corresponding category
data_clean$Size <- ifelse(
  is.na(data_clean$Size),  # Check if Size is NA
  round(mean_size_by_type[data_clean$Category], 1),  # Replace with the mean size based on the Category
  data_clean$Size  # Keep the original size if it's not NA
)

Installs

####Remove the ‘+’ sign, Remove the commas, Convert to numeric

#clean installations
clean_installs <- function(Installs) {
  Installs <- gsub("\\+", "", Installs)  
  Installs <- gsub(",", "", Installs)    
  return(as.numeric(Installs))           
}

data_clean$Installs <- sapply(data_clean$Installs, clean_installs)

nan_rows <- sapply(data_clean[, c("Size", "Installs")], function(x) any(is.nan(x)))

# Display only rows that contain NaN in either Size or Installs
data_clean[,nan_rows]

## data frame with 0 columns and 9659 rows

datatable((data_clean), options = list(scrollX = TRUE ))

Display the unique values

data_clean <- data_clean %>%
  mutate(Rating = ifelse(is.na(Rating), mean(Rating, na.rm = TRUE), Rating))

# Identify the unique values in the 'Installs' column
unique_values <- unique(data_clean$Installs)

# Display the unique values
print(unique_values)

##  [1] 1e+04 5e+05 5e+06 5e+07 1e+05 5e+04 1e+06 1e+07 5e+03 1e+08 1e+09 1e+03
## [13] 5e+08 5e+01 1e+02 5e+02 1e+01 1e+00 5e+00 0e+00

# Function to convert the installs to numeric
convert_to_numeric <- function(x) {
  # Remove non-numeric characters and convert to numeric
  as.numeric(gsub("[^0-9]", "", x)) * 10^(length(gregexpr(",", x)[[1]]) - 1)
}

# Sort unique values based on the custom numeric conversion
sorted_values <- unique_values[order(sapply(unique_values, convert_to_numeric))]

Rating and Reviews

# Checking the type of the Rating 
typeof(data_clean$Rating)

## [1] "double"

# Checking the type of the Reviews 
typeof(data_clean$Reviews)

## [1] "character"

Checking the format of Rating and Reviews

##  chr [1:9659] "159" "967" "87510" "215644" "967" "167" "178" "36815" ...

##  num [1:9659] 4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...

As we can see the Review column is in string format which could be converted into int for more insights.

Change the column reviews from Str to int

## 'data.frame':    9659 obs. of  13 variables:
##  $ App           : chr  "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
##  $ Category      : chr  "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" ...
##  $ Rating        : num  4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
##  $ Reviews       : num  159 967 87510 215644 967 ...
##  $ Size          : num  19 14 8.7 25 2.8 5.6 19 29 33 3.1 ...
##  $ Installs      : num  1e+04 5e+05 5e+06 5e+07 1e+05 5e+04 5e+04 1e+06 1e+06 1e+04 ...
##  $ Type          : chr  "Free" "Free" "Free" "Free" ...
##  $ Price         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Content.Rating: chr  "Everyone" "Everyone" "Everyone" "Teen" ...
##  $ Genres        : chr  "Art & Design" "Art & Design;Pretend Play" "Art & Design" "Art & Design" ...
##  $ Last.Updated  : chr  "January 7, 2018" "January 15, 2018" "August 1, 2018" "June 8, 2018" ...
##  $ Current.Ver   : chr  "1.0.0" "2.0.0" "1.2.4" "Varies with device" ...
##  $ Android.Ver   : chr  "4.0.3 and up" "4.0.3 and up" "4.0.3 and up" "4.2 and up" ...

Table: Statistics summary.
	App	Category	Rating	Reviews	Size	Installs	Type	Price	Content.Rating	Genres	Last.Updated	Current.Ver	Android.Ver
Min	Length:9659	Length:9659	Min. :1.000	Min. : 0	Min. : 0.0085	Min. :0.000e+00	Length:9659	Min. : 0.000	Length:9659	Length:9659	Length:9659	Length:9659	Length:9659
Q1	Class :character	Class :character	1st Qu.:4.000	1st Qu.: 25	1st Qu.: 5.3000	1st Qu.:1.000e+03	Class :character	1st Qu.: 0.000	Class :character	Class :character	Class :character	Class :character	Class :character
Median	Mode :character	Mode :character	Median :4.200	Median : 967	Median : 13.1000	Median :1.000e+05	Mode :character	Median : 0.000	Mode :character	Mode :character	Mode :character	Mode :character	Mode :character
Mean	NA	NA	Mean :4.173	Mean : 216593	Mean : 20.1512	Mean :7.778e+06	NA	Mean : 1.099	NA	NA	NA	NA	NA
Q3	NA	NA	3rd Qu.:4.500	3rd Qu.: 29401	3rd Qu.: 27.0000	3rd Qu.:1.000e+06	NA	3rd Qu.: 0.000	NA	NA	NA	NA	NA
Max	NA	NA	Max. :5.000	Max. :78158306	Max. :100.0000	Max. :1.000e+09	NA	Max. :400.000	NA	NA	NA	NA	NA

There are 1463 missing values in rating.

As it could observed the Family category apps have the highest NA values. Let’s not drop them but handle them by replacing with the mean value for the category.

#Replace NA in Ratings with Overall Mean
data_clean <- data_clean %>%
  mutate(Rating = ifelse(is.na(Rating), mean(Rating, na.rm = TRUE), Rating))

xkablesummary(data_clean)

Table: Statistics summary.
	App	Category	Rating	Reviews	Size	Installs	Type	Price	Content.Rating	Genres	Last.Updated	Current.Ver	Android.Ver
Min	Length:9659	Length:9659	Min. :1.000	Min. : 0	Min. : 0.0085	Min. :0.000e+00	Length:9659	Min. : 0.000	Length:9659	Length:9659	Length:9659	Length:9659	Length:9659
Q1	Class :character	Class :character	1st Qu.:4.000	1st Qu.: 25	1st Qu.: 5.3000	1st Qu.:1.000e+03	Class :character	1st Qu.: 0.000	Class :character	Class :character	Class :character	Class :character	Class :character
Median	Mode :character	Mode :character	Median :4.200	Median : 967	Median : 13.1000	Median :1.000e+05	Mode :character	Median : 0.000	Mode :character	Mode :character	Mode :character	Mode :character	Mode :character
Mean	NA	NA	Mean :4.173	Mean : 216593	Mean : 20.1512	Mean :7.778e+06	NA	Mean : 1.099	NA	NA	NA	NA	NA
Q3	NA	NA	3rd Qu.:4.500	3rd Qu.: 29401	3rd Qu.: 27.0000	3rd Qu.:1.000e+06	NA	3rd Qu.: 0.000	NA	NA	NA	NA	NA
Max	NA	NA	Max. :5.000	Max. :78158306	Max. :100.0000	Max. :1.000e+09	NA	Max. :400.000	NA	NA	NA	NA	NA

Now there are no missing values in reviews.

Checking for Outliers For rating by seeing frequency for each rating

 breaks = seq(15,20,by = 1)
frequency_table = table(data_clean$Rating)
frequency_table

## 
##                1              1.2              1.4              1.5 
##               16                1                3                3 
##              1.6              1.7              1.8              1.9 
##                4                8                8               11 
##                2              2.1              2.2              2.3 
##               12                8               14               20 
##              2.4              2.5              2.6              2.7 
##               19               20               24               23 
##              2.8              2.9                3              3.1 
##               40               45               81               69 
##              3.2              3.3              3.4              3.5 
##               63              100              126              156 
##              3.6              3.7              3.8              3.9 
##              167              224              286              359 
##                4              4.1 4.17324304538799              4.2 
##              513              621             1463              810 
##              4.3              4.4              4.5              4.6 
##              897              895              848              683 
##              4.7              4.8              4.9                5 
##              442              221               85              271

From above it can be seen all the rating are between 1 and 5.

Current Version & Genres

Due to the inconsistent formatting of values in the Current.Ver column, this column is dropped and will be excluded from the analysis.

data_final <- data_clean %>% select(-c('Genres', 'Current.Ver'))
data_final$Category <- factor(data_final$Category)
data_final$Android.Ver <- factor(data_final$Android.Ver)

Content Rating, Last Updated

# Remove leading and trailing spaces and convert all text to a consistent format 
data_final$Content.Rating <- trimws(tolower(data_final$Content.Rating))

cr_missing <- sum(is.na(data_final$`Content Rating`))

print(paste("Number of missing values in 'Content Rating':", cr_missing))

## [1] "Number of missing values in 'Content Rating': 0"

There are no missing values for Content rating.

# Convert Last Updated to Date format
data_final$Last.Updated <- as.Date(data_final$Last.Updated, format = "%B %d, %Y")

# Verify the cleaning
print("\nSummary of Last.Updated after cleaning:")

## [1] "\nSummary of Last.Updated after cleaning:"

print(summary(data_clean$Last.Updated))

##    Length     Class      Mode 
##      9659 character character

After cleaning the Data

str(data_final)

## 'data.frame':    9659 obs. of  11 variables:
##  $ App           : chr  "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
##  $ Category      : Factor w/ 33 levels "ART_AND_DESIGN",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Rating        : num  4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
##  $ Reviews       : num  159 967 87510 215644 967 ...
##  $ Size          : num  19 14 8.7 25 2.8 5.6 19 29 33 3.1 ...
##  $ Installs      : num  1e+04 5e+05 5e+06 5e+07 1e+05 5e+04 5e+04 1e+06 1e+06 1e+04 ...
##  $ Type          : chr  "Free" "Free" "Free" "Free" ...
##  $ Price         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Content.Rating: chr  "everyone" "everyone" "everyone" "teen" ...
##  $ Last.Updated  : Date, format: "2018-01-07" "2018-01-15" ...
##  $ Android.Ver   : Factor w/ 34 levels "1.0 and up","1.5 and up",..: 16 16 16 19 21 9 16 19 11 16 ...

Data Exploring and Visualization

Visualization for Price Distribution

# Count Plot for the Price distribution
ggplot(data_final, aes(x=Price)) +
  geom_histogram(binwidth=2, fill="pink", color="black") +
   xlim(0, 500) + ylim(0, 500) +
  labs(title="Price Distribution", x="Price", y="Frequency") +
  theme_minimal()

The data is highly skewed as there are many zero price entries.

# Boxplot for the same
ggplot(data_final, aes(y=Price)) +
  geom_boxplot(outlier.colour = "red", outlier.shape = 16, outlier.size = 1, fill="pink", color="black") +
  labs(title="Price Boxplot", y="Price") +
  theme_minimal()

Checking outliers for Price

outlierKD2 <- function(df, var, rm = FALSE, boxplt = FALSE, histogram = TRUE, qqplt = FALSE) {
  dt <- df  # Duplicate the dataframe for potential alteration
  var_name <- eval(substitute(var), eval(dt))
  na1 <- sum(is.na(var_name))
  m1 <- mean(var_name, na.rm = TRUE)
  colTotal <- boxplt + histogram + qqplt  # Calculate the total number of charts to be displayed
  par(mfrow = c(2, max(2, colTotal)), oma = c(0, 0, 3, 0))  # Adjust layout for plots
  
  # Q-Q plot with custom title
  if (qqplt) {
    qqnorm(var_name, main="Q-Q plot without Outliers")
    qqline(var_name)
  }
  
  # Histogram with custom title
  if (histogram) { 
    hist(var_name,main = "Histogram without Outliers", xlab = NA, ylab = NA) 
  }
  
  # Box plot with custom title
  if (boxplt) { 
    boxplot(var_name, main= "Box Plot without Outliers")
  }
  
  # Identify outliers
  outlier <- boxplot.stats(var_name)$out
  mo <- mean(outlier)
  var_name <- ifelse(var_name %in% outlier, NA, var_name)
  
  # Q-Q plot without outliers
  if (qqplt) {
    qqnorm(var_name, main="Q-Q plot with Outliers")
    qqline(var_name)
  }
  
  # Histogram without outliers
  if (histogram) { 
    hist(var_name, main = "Histogram with Outliers", xlab = NA, ylab = NA) 
  }
  
  # Box plot without outliers
  if (boxplt) { 
    boxplot(var_name, main = "Boxplot with Outliers") 
  }
  
  # Add the title for the overall plot section if any plots are displayed
  if (colTotal > 0) {
    title("Outlier Check", outer = TRUE)
    na2 <- sum(is.na(var_name))
    cat("Outliers identified:", na2 - na1, "\n")
    cat("Proportion (%) of outliers:", round((na2 - na1) / sum(!is.na(var_name)) * 100, 1), "\n")
    cat("Mean of the outliers:", round(mo, 2), "\n")
    cat("Mean without removing outliers:", round(m1, 2), "\n")
    cat("Mean if we remove outliers:", round(mean(var_name, na.rm = TRUE), 2), "\n")
  }
}

#outlier function is defined in previous chunck of code.
outlier_check_price = outlierKD2(data_final, Price, rm = FALSE, boxplt = TRUE, qqplt = TRUE)

## Outliers identified: 756 
## Proportion (%) of outliers: 8.5 
## Mean of the outliers: 14.05 
## Mean without removing outliers: 1.1 
## Mean if we remove outliers: 0

The price values in the dataset, including both typical and extreme values, are valid observations for our analysis. As such, removing these outliers may not be beneficial for our study.

#To check the value ranges
table(data_final$Price)

## 
##      0   0.99      1   1.04    1.2   1.26   1.29   1.49    1.5   1.59   1.61 
##   8903    145      3      1      1      1      1     46      1      1      1 
##    1.7   1.75   1.76   1.96   1.97   1.99      2   2.49    2.5   2.56   2.59 
##      2      1      1      1      1     73      3     25      1      1      1 
##    2.6    2.9   2.95   2.99   3.02   3.04   3.08   3.28   3.49   3.61   3.88 
##      1      1      1    124      1      1      1      1      7      1      1 
##    3.9   3.95   3.99   4.29   4.49   4.59    4.6   4.77    4.8   4.84   4.85 
##      1      1     57      1      9      1      1      1      1      1      1 
##   4.99      5   5.49   5.99   6.49   6.99   7.49   7.99   8.49   8.99      9 
##     70      1      5     26      5     11      2      7      2      5      1 
##   9.99     10  10.99  11.99  12.99  13.99     14  14.99  15.46  15.99  16.99 
##     19      2      2      3      4      2      1      9      1      1      2 
##  17.99  18.99   19.4   19.9  19.99  24.99  25.99  28.99  29.99  30.99  33.99 
##      2      1      1      1      5      3      1      1      5      1      1 
##  37.99  39.99  46.99  74.99  79.99  89.99 109.99 154.99    200 299.99 379.99 
##      1      2      1      1      1      1      1      1      1      1      1 
## 389.99 394.99 399.99    400 
##      1      1     12      1

As aldready mentioned, there are 8903 free apps (More apps with price as 0).

Visualization for Type Distribution

# Bar Plot for the Type Distribution
ggplot(data_final, aes(x = Type)) +
  geom_bar(fill = "pink", color = "black") +
  labs(title = "Distribution of App Types (Free vs Paid)", x = "Type", y = "Count") +
  theme_minimal()

As it is clear, there are more free apps.

#Display statistics for the Price of apps grouped by their Type
data_final$Type <- as.factor(data_final$Type)


summary_by_type <- data.frame(
  Type = levels(data_final$Type),
  Min_Price = tapply(data_clean$Price, data_clean$Type, min, na.rm = TRUE),
  Max_Price = tapply(data_clean$Price, data_clean$Type, max, na.rm = TRUE),
  Mean_Price = tapply(data_clean$Price, data_clean$Type, mean, na.rm = TRUE),
  Median_Price = tapply(data_clean$Price, data_clean$Type, median, na.rm = TRUE)
)


print(summary_by_type)

##      Type Min_Price Max_Price Mean_Price Median_Price
## Free Free      0.00         0    0.00000         0.00
## NaN   NaN      0.00         0    0.00000         0.00
## Paid Paid      0.99       400   14.04515         2.99

#Scatter plot for price distribution by app type
ggplot(data_final, aes(x = Type, y = Price, fill = Type)) +
  geom_boxplot() +
  labs(title = "Price Distribution by App Type", 
       x = "App Type", 
       y = "Price ($)") +
  theme_minimal()

Histogram for price distribution by App Type

ggplot(data_final, aes(x = Price, fill = Type)) +
  geom_histogram(binwidth = 60, alpha = 0.7, position = "identity") +
  facet_wrap(~ Type) +
  labs(title = "Price Distribution by App Type", 
       x = "Price ($)", 
       y = "Count") +
  theme_minimal()

Upon analyzing the price distribution across different app types, we found that some values in the Type column do not accurately represent the app prices (from above plot). Since we can fully rely on the Price values for our analysis, the Type column is seemed unnecessary.

Hence, Removing the Type column…

Dropping the Type column

#Using subset function
data_final <- subset(data_final, select = -Type)

#After removing the Type column and duplicated values
str(data_final)

## 'data.frame':    9659 obs. of  10 variables:
##  $ App           : chr  "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
##  $ Category      : Factor w/ 33 levels "ART_AND_DESIGN",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Rating        : num  4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
##  $ Reviews       : num  159 967 87510 215644 967 ...
##  $ Size          : num  19 14 8.7 25 2.8 5.6 19 29 33 3.1 ...
##  $ Installs      : num  1e+04 5e+05 5e+06 5e+07 1e+05 5e+04 5e+04 1e+06 1e+06 1e+04 ...
##  $ Price         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Content.Rating: chr  "everyone" "everyone" "everyone" "teen" ...
##  $ Last.Updated  : Date, format: "2018-01-07" "2018-01-15" ...
##  $ Android.Ver   : Factor w/ 34 levels "1.0 and up","1.5 and up",..: 16 16 16 19 21 9 16 19 11 16 ...

The Type column is successfully removed.

Let’s do bivariate analysis on price and other variables starting from here.

Visualization for Price vs Installs

#Plotting a scatter plot between Price and installs
ggplot(data_final, aes(x=Price, y=log(data_clean$Installs))) +
  geom_point(color = 'red', size = 1, alpha = 0.5) + 
  geom_smooth(method = 'lm', color = 'blue', se = FALSE) +
  labs(title = "Price vs Installs", x = "Price (USD)", y = "Number of Installs") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

From the scatter plot, we can see that there are more number of installations with price value 0.

# Categorize the apps as "Free" or "Paid" based on Price
Price_Category <- ifelse(data_final$Price == 0, "Free", "Paid")
str(data_final$Price)

##  num [1:9659] 0 0 0 0 0 0 0 0 0 0 ...

str(Price_Category)

##  chr [1:9659] "Free" "Free" "Free" "Free" "Free" "Free" "Free" "Free" ...

#str(log(data_clean$Installs))

For a better visualization, we are categorizing price values 0 as free apps and plotting abox plot.

# Box plot of Price Category vs. log-transformed Installs
ggplot(data_final, aes(x = Price_Category, y = log(data_clean$Installs))) +
  geom_boxplot(fill = "lightblue", color = "darkblue", alpha = 0.6) +
  labs(title = "Price Categories vs. Log-Transformed Installs", 
       x = "Price Category", 
       y = "Log(Installs)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

“Free” apps tend to have more installs than “Paid” apps. The difference between the means on the log scale is estimated to be between 3.47 and 3.97.

# Categorize the apps as "Free" or "Paid" based on Price
Price_Category <- ifelse(data_final$Price == 0, "Free", "Paid")
str(data_final$Price)

##  num [1:9659] 0 0 0 0 0 0 0 0 0 0 ...

str(Price_Category)

##  chr [1:9659] "Free" "Free" "Free" "Free" "Free" "Free" "Free" "Free" ...

#str(data_final$log(data_clean$Installs))

table(Price_Category)

## Price_Category
## Free Paid 
## 8903  756

# Add Price_Category to data_final
data_duplicate <- data_final
data_duplicate$Price_Category <- ifelse(data_final$Price == 0, "Free", "Paid")

# Create a summarized table for Price_Category and log_Installs
summary_table <- data_duplicate %>%
  group_by(Price_Category) %>%
  summarise(Average_Log_Installs = mean(log(data_clean$Installs), na.rm = TRUE),
            Count = n())

# View the summarized table
kable(summary_table, format = "html", col.names = c("Price Category", "Mean Log(Installs)", "App Count")) %>%
  kable_styling(full_width = FALSE, position = "center")

Price Category	Mean Log(Installs)	App Count
Free	-Inf	8903
Paid	-Inf	756

Visualization for Price vs Reviews & Rating

# Plot Price vs. Reviews
ggplot(data_final, aes(x=Price, y=Reviews)) +
  geom_point(color = 'blue') +
  geom_smooth(method = 'lm', color = 'red', se = FALSE) +
  labs(title = "Price vs Reviews", x = "Price (USD)", y = "Number of Reviews") +
  theme_minimal() + 
  theme(
    panel.background = element_rect(fill = "white"),  # Set panel background to white
    plot.background = element_rect(fill = "white"),   # Set plot background to white
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

# Plot Price vs. Rating
ggplot(data_final, aes(x=Price, y=Rating)) +
  geom_point(color = 'green') +
  geom_smooth(method = 'lm', color = 'red', se = FALSE) +
  labs(title = "Price vs Rating", x = "Price (USD)", y = "Rating") +
  theme_minimal() + 
  theme(
    panel.background = element_rect(fill = "white"),  # Set panel background to white
    plot.background = element_rect(fill = "white"),   # Set plot background to white
    axis.text.x = element_text(angle = 45, hjust = 1)
  )

Price vs Reviews with installation: Cheaper products tend to have more reviews, indicating higher popularity or more frequent purchases. In contrast, expensive products tend to have fewer reviews, possibly because fewer people buy higher-priced items.

Price vs Ratings with installation: Price does not strongly affect the average rating, but there is a slight trend where lower-priced products have more variation in ratings, while higher-priced products tend to receive more consistent ratings around 4. May be higher price apps are meeting the customer expectations.

Visualization for Price vs Reviews vs Installs

# Scatter plot of Price vs. Ratings with log_Installs as  color
ggplot(data_final, aes(x = Price, y = Rating,color = log(data_clean$Installs))) +
  geom_point(alpha = 0.6) +
  scale_color_gradient(low = "blue", high = "red") +  
  labs(title = "Price vs. Ratings with Installs as Color by Price", 
       x = "Price", 
       y = "Rating", 
       color = "log(Installs)") +
  theme_minimal()

# Scatter plot of Price vs. Reviews with log_Installs as color
ggplot(data_final, aes(x = Price, y = Reviews,color = log(data_clean$Installs))) +
  geom_point(alpha = 0.6) +
  scale_color_gradient(low = "darkgreen", high = "yellow") +  
  labs(title = "Price vs. reviewss with Installs as Color by Price", 
       x = "Price", 
       y = "Reviews", 
       color = "log(Installs)") +
  theme_minimal()

Concluding: Apps with lower prices, have more ratings and installs while apps priced higher tend to have fewer installs and more scattered ratings. Similarly, for reviews.

Visualization for Price vs Size

# Plot Price vs Size
ggplot(data_final, aes(x=Price, y=Size)) +
  geom_point(color = 'red') + 
  geom_smooth(method = 'lm', color = 'blue', se = FALSE) +
  labs(title = "Price vs Size", x = "Price (USD)", y = "App Size (MB)") +
  theme_minimal()

Visulization for Distribution of Installs

# Create a new data frame to store the factor levels
data_clean1_factor <- data_final  # Assuming you want to keep the original data intact
data_clean1_factor$Installs <- factor(data_final$Installs, levels = sorted_values)
# Define new breaks for more even intervals for Installs
install_breaks <- c(c(0, 500, 1000, 5000, 10000, 50000, 100000, 300000, 1000000, 5000000,10000000, Inf))

# Create a categorical variable for installs based on these breaks
data_clean1_factor$Installs_Category <- cut(
  as.numeric(as.character(data_final$Installs)), 
  breaks = install_breaks, 
  right = FALSE, 
  labels = c("0+", "500+", "1K+", "5K+", "10K+", "50K+", "100K+", "300K+", "1M+", "5M+","Above 10M+")
)


# Plot the categorized Installs data
library(ggplot2)
ggplot(data_clean1_factor, aes(x = Installs_Category)) +
  geom_bar() +
  xlab("Installs") +
  ylab("Count") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  ggtitle("Distribution of App Installs by Category")

#### Installs vs Size

ggplot(data_clean, aes(x = Size, y = log(Installs))) +
  geom_hex(bins = 30) +
  scale_fill_viridis_c() + # Adds color gradient
  labs(title = "Plot of App Size vs. Installs (Log Scale)",
       x = "Size (MB)",
       y = "Installs (Log Scale)") +
  theme_minimal()

Visualization for Rating Distribution

boxplot(data_final$Rating,ylab = "Rating", xlab = "Count",col = "Blue")

hist(data_clean$Rating, main="Histogram of Apps Rating after cleaning", xlab="Rating (count)", col = 'blue', breaks = 100 )

qqnorm(data_clean$Rating)
qqline(data_clean$Rating, col = "red")

Here, it could be seen the plots are much clearer but still skewed due to other outliers from 1-3 rating but as these may be the reason from which we could find why the apps are low rated hencecannot be removed from our dataset.

Visualization for Reviews

boxplot(data_final$Reviews,ylab = "Reviews", xlab = "Count",col = 'Blue')

hist(data_final$Reviews, main="Histogram of Apps Reviews", xlab="Reviews (count)", col = 'blue', breaks = 100 )

ggplot(data_final, aes(x = log(Reviews))) +
  geom_histogram(binwidth = 0.1, fill = "blue", color = "black") +
  labs(title = "Log-Transformed Histogram of Ratings", x = "Log(Rating)", y = "Count")

qqnorm(data_final$Reviews)
qqline(data_final$Reviews, col = "red")

Similar to the case of ratings the plots are skewed due to the outliers. Hence, we can use the log plot of reviews for the visualisation which is normalised version of Reviews. As they are skewed, they donot follow normal distribution.

Review frequency table

xkablesummary(data_final)

Table: Statistics summary.
	App	Category	Rating	Reviews	Size	Installs	Price	Content.Rating	Last.Updated	Android.Ver
Min	Length:9659	FAMILY :1832	Min. :1.000	Min. : 0	Min. : 0.0085	Min. :0.000e+00	Min. : 0.000	Length:9659	Min. :2010-05-21	4.1 and up :2202
Q1	Class :character	GAME : 959	1st Qu.:4.000	1st Qu.: 25	1st Qu.: 5.3000	1st Qu.:1.000e+03	1st Qu.: 0.000	Class :character	1st Qu.:2017-08-05	4.0.3 and up :1395
Median	Mode :character	TOOLS : 827	Median :4.200	Median : 967	Median : 13.1000	Median :1.000e+05	Median : 0.000	Mode :character	Median :2018-05-04	4.0 and up :1285
Mean	NA	BUSINESS : 420	Mean :4.173	Mean : 216593	Mean : 20.1512	Mean :7.778e+06	Mean : 1.099	NA	Mean :2017-10-30	Varies with device: 990
Q3	NA	MEDICAL : 395	3rd Qu.:4.500	3rd Qu.: 29401	3rd Qu.: 27.0000	3rd Qu.:1.000e+06	3rd Qu.: 0.000	NA	3rd Qu.:2018-07-17	4.4 and up : 818
Max	NA	PERSONALIZATION: 376	Max. :5.000	Max. :78158306	Max. :100.0000	Max. :1.000e+09	Max. :400.000	NA	Max. :2018-08-08	2.3 and up : 616
NA	NA	(Other) :4850	NA	NA	NA	NA	NA	NA	NA	(Other) :2353

outlierKD2(data_final, Reviews)

## Outliers identified: 1656 
## Proportion (%) of outliers: 20.7 
## Mean of the outliers: 1228141 
## Mean without removing outliers: 216592.6 
## Mean if we remove outliers: 7280.61

To check which are outliers lets make sections of data that is create bins to check which bins have maximum data, this would help us see how reviews are distributed.

Binned reviews

Binning into equal count in each bin to check averge rating for each bin

# Define the new custom breaks for bins
# Ensure there are no NA values


# Define new breaks for more even intervals
breaks <- c(0, 100, 500, 1000, 2500, 5000, 10000, 25000,50000,100000, 300000,1000000,Inf)

# Create a categorical variable based on the new breaks
Review_Category <- cut(data_final$Reviews, breaks = breaks, right = FALSE, 
                   labels = c("0+","100+", "500+", "1K+",
                              "2.5K+", "5K+", "10K+","25K+",
                              "50K+", "100K+","300K+","1M+"))

# Count the number of values in each bin
bin_counts <- as.data.frame(table(Review_Category))

# Rename the columns for clarity
colnames(bin_counts) <- c("Review_Category", "Count")

# Print the counts
print(bin_counts)

##    Review_Category Count
## 1               0+  3327
## 2             100+  1065
## 3             500+   462
## 4              1K+   586
## 5            2.5K+   475
## 6              5K+   474
## 7             10K+   719
## 8             25K+   606
## 9             50K+   498
## 10           100K+   647
## 11           300K+   451
## 12             1M+   349

# Create a line plot of the binned counts
ggplot(bin_counts, aes(x = Review_Category, y = Count, group = 1)) +
  geom_line(color = "blue", size = 1) +
  geom_point(color = "blue", size = 3) +
  labs(title = "Count of Reviews by Review Category", 
       x = "Review Category", 
       y = "Count of Reviews") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

Hence, high reviews can be observed in less apps and less reviews can be observed in more apps which is expected.

Boxplots for Rating vs Reviews

boxplot( data_final$Rating~ Review_Category, data = data_clean, 
        main = "Boxplot of Review Counts by Review Category", 
        xlab = "Review Category", 
        ylab = "Review Rating",
        las = 2,        # Rotate the x-axis labels for readability
        col = "lightblue")  # Optional: Set color for the boxplots

In this we could observe that, as reviews increase the median of rating increased and the values clustered around higher ratings which could show that high reviews, could mean a better rated app.

Mean value of Ratings for each Review bins

# Calculate the mean Rating for each Review_Category
mean_ratings <- tapply(data_final$Rating, Review_Category, mean, na.rm = TRUE)

# Convert the result to a data frame for better readability
mean_ratings_df <- data.frame(Review_Category = names(mean_ratings), Mean_Rating = as.numeric(mean_ratings))

# Print the mean ratings for each review bin
print(mean_ratings_df)

##    Review_Category Mean_Rating
## 1               0+    4.126221
## 2             100+    4.029538
## 3             500+    4.063188
## 4              1K+    4.107030
## 5            2.5K+    4.129572
## 6              5K+    4.191139
## 7             10K+    4.221836
## 8             25K+    4.231848
## 9             50K+    4.293775
## 10           100K+    4.329830
## 11           300K+    4.375610
## 12             1M+    4.426361

# Define correct order of Review_Category as a factor
mean_ratings_df$Review_Category <- factor(mean_ratings_df$Review_Category, 
                                          levels = c("0+","100+", "500+", "1K+",
                                                     "2.5K+", "5K+", "10K+","25K+",
                                                     "50K+", "100K+", "300K+", "1M+"))

# Plot the mean ratings for each review bin in the correct order
ggplot(mean_ratings_df, aes(x = Review_Category, y = Mean_Rating)) +
  geom_bar(stat = "identity", fill = "steelblue") +  # Use bar plot
  labs(title = "Mean Rating by Review Category",
       x = "Review Category",
       y = "Mean Rating") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-axis labels for readability

As we can see, the mean rating increases as the reviews increase.

Histogram for Reviews and Rating

# Create a new data frame for plotting
plot_data <- data.frame(Rating = data_final$Rating, Review_Category = Review_Category)

# Create a histogram of Ratings, faceted by Review_Category
ggplot(plot_data, aes(x = Rating)) +
  geom_histogram(bins = 30, fill = "blue", alpha = 0.7) +
  facet_wrap(~ Review_Category, labeller = label_wrap_gen()) +  # Facet by Review_Category
  theme_minimal() +
  labs(title = "Histograms of Ratings by Review Category", x = "Rating", y = "Frequency")

This is another representation of ratings vs reviews.

Visualization for Reviews vs Installs

# Scatter plot for Installs vs Reviews
ggplot(data_clean1_factor, aes(x = Reviews, y = Installs)) +
  geom_point(color = "blue", alpha = 0.5) +
  labs(title = "Scatter Plot of Installs vs Reviews", 
       x = "Number of Reviews", 
       y = "Number of Installs") +
  theme_minimal()

Visualization for Rating vs Installs

# Scatter plot of log-transformed Installs vs. Rating
ggplot(data_final, aes(x = log(data_clean$Installs), y = Rating)) +
  geom_point(color = "blue", alpha = 0.6) +
  geom_smooth(method = "lm", color = "red", se = FALSE) +  # Add a regression line
  labs(title = "Log-Transformed Installs vs. Rating", 
       x = "Log(Installs)", 
       y = "Rating") +
  theme_minimal()

Visualization for Rating vs Installs by Category

Visualization for Category Distribution

category_counts <- table(data_final$Category)

# Convert to data frame for plotting
category_counts_df <- as.data.frame(category_counts)
colnames(category_counts_df) <- c("Category", "Frequency") 

ggplot(category_counts_df, aes(x = reorder(Category, Frequency), y = Frequency)) + 
  geom_bar(stat = "identity", fill = "#1f3374") +
  geom_text(aes(label = Frequency), vjust = 0.5, hjust=1, size=2.5, color='#f8c220') +
  coord_flip() +  
  labs(title = "Distribution of Categories", x = "Category", y = "Frequency") +
  theme_minimal() +
   theme(
    plot.background = element_rect(fill = "#efefef", color = NA),
    panel.background = element_rect(fill = "#efefef", color = NA),
    axis.text.y = element_text(size = 5.5)
  )

AS it can be seen from the graph above, most of the apps in the dataset belong to the Family category, and Beauty has the least number of apps.

Visualization for Category vs. Installs

Below is a boxplot show the distribution of number of installs for each category order by mean from highest to lowest.

ggplot(data_clean, aes(x = reorder(Category, log(data_final$Installs),  FUN = mean), y = log(data_clean$Installs))) +
  geom_boxplot(outlier.color = "#f05555", outlier.shape = 1, color='#1f3374', fill="#efefef") +  # Red outliers for emphasis
  coord_flip() +  # Flip for better readability
  scale_y_log10() +
  theme_minimal() +
  labs(title = "Distribution of Installs by Category",
       x = "Category",
       y = "Number of Installs (Log Scale)") +
    theme(
    plot.background = element_rect(fill = "#efefef", color = NA),
    panel.background = element_rect(fill = "#efefef", color = NA),
    axis.text.y = element_text(size = 5.5)
  )

It can be seen from the graph that, on average, Entertainment apps receive the highest number of installations, followed by Education, Game, Photography, and Weather apps. In contrast, Art & Design apps have the fewest installations.

Visualization for Category vs. App Size

Below is the figure showing the distribution of app sizes in each category.

#df_clean <- data_clean %>%
 # mutate(Size = sapply(Size, convert_size)) %>%
#  filter(!is.na(Size))

# Plot the histogram with faceting by category
ggplot(data_clean, aes(x = Size)) +
  geom_histogram(binwidth = 5, fill = "#304ba6", color = "black") +
  facet_wrap(~ Category, scales = "free_y") +
  theme_minimal() +
  labs(
    title = "Distribution of App Sizes by Category",
    x = "Size (MB)",
    y = "Count"
  ) +
  theme(
    strip.text = element_text(size = 5),
    axis.text.x = element_text(size = 7, angle = 45, hjust = 1)
  )

str(data_clean)

## 'data.frame':    9659 obs. of  13 variables:
##  $ App           : chr  "Photo Editor & Candy Camera & Grid & ScrapBook" "Coloring book moana" "U Launcher Lite – FREE Live Cool Themes, Hide Apps" "Sketch - Draw & Paint" ...
##  $ Category      : chr  "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" "ART_AND_DESIGN" ...
##  $ Rating        : num  4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
##  $ Reviews       : num  159 967 87510 215644 967 ...
##  $ Size          : num  19 14 8.7 25 2.8 5.6 19 29 33 3.1 ...
##  $ Installs      : num  1e+04 5e+05 5e+06 5e+07 1e+05 5e+04 5e+04 1e+06 1e+06 1e+04 ...
##  $ Type          : chr  "Free" "Free" "Free" "Free" ...
##  $ Price         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Content.Rating: chr  "Everyone" "Everyone" "Everyone" "Teen" ...
##  $ Genres        : chr  "Art & Design" "Art & Design;Pretend Play" "Art & Design" "Art & Design" ...
##  $ Last.Updated  : chr  "January 7, 2018" "January 15, 2018" "August 1, 2018" "June 8, 2018" ...
##  $ Current.Ver   : chr  "1.0.0" "2.0.0" "1.2.4" "Varies with device" ...
##  $ Android.Ver   : chr  "4.0.3 and up" "4.0.3 and up" "4.0.3 and up" "4.2 and up" ...

ggplot(data_clean, aes(x = reorder(Category, Size, FUN = median), y = Size)) + 
  geom_boxplot(outlier.color = "#f05555", outlier.shape = 1) + 
  coord_flip() + 
  theme_minimal() + 
  labs(
    title = "Boxplot of App Sizes by Category (Ordered by Median)", 
    x = "Category", 
    y = "Size (MB)"
  ) + 
  theme(
    strip.text = element_text(size = 8), 
    axis.text.x = element_text(size = 7, angle = 45, hjust = 1)
  )

As it can be seen from the two figures above, most categories exhibit right-skewed app sizes, with the majority being under 50MB. However, the Game category stands out with a significantly larger median app size compared to other categories.

Visualization for Category vs. Reviews

Below is the graph displaying the distribution of reviews left by users for each category.

df_aggregated <- data_final %>% 
  group_by(Category) %>% 
  summarise(Total_Reviews = sum(Reviews, na.rm = TRUE))

#df_aggregated

# Plot the total reviews by category using a bar chart
ggplot(df_aggregated, aes(x = reorder(Category, -Total_Reviews), y = log10(Total_Reviews))) + 
  geom_bar(stat = "identity", fill = "#1f3374") + 
  labs(
    title = "Log-Scaled Total Reviews by Category", 
    x = "Category", 
    y = "Log10(Total Number of Reviews)"
  ) + 
  theme_minimal() + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

AS it can be seen that game apps have most reviews while events apps have the least reviews.

Histogram for Category vs. Rating

Below is the figure demonstrating the distribution of number of rating for each category.

ggplot(data_final, aes(x = Rating)) + 
  geom_histogram(binwidth = 0.5, fill = "#1f3374", color='#efefef') + 
  facet_wrap(~ Category, scales = "free_y") +  # Facet by Category with independent y-axis
  scale_x_continuous(limits = c(1, 5), breaks = seq(1, 5, by = 0.5)) +  # Restrict x-axis to 1-5
  theme_minimal() + 
  labs(
    title = "Distribution of Ratings by Category", 
    x = "Rating", 
    y = "Count"
  ) + 
  theme(
    strip.text = element_text(size = 5),  # Adjust facet label size
    axis.text.x = element_text(size = 5, angle = 45, hjust = 1),  # Rotate x-axis labels
    plot.title = element_text(hjust = 0.5)  # Center the plot title
  )

As illustrated in the graph above, all categories have app ratings that range between 4.0 and 5.0.

Visualization for Android Version

Below is the figure showing the distribution of Android versions.

df_clean <- data_final %>% 
  filter(!is.na(Android.Ver) & !is.na(Reviews) & !(Android.Ver == 'NaN'))

extract_version <- function(version) {
  version <- tolower(version)  # Make lowercase for consistency
  
  # Handle "Varies with device" and "NaN"
  if (version == "varies with device" || version == "nan") return(NA)
  
  # Extract the first version in case of ranges (e.g., "4.1 - 7.1.1" -> "4.1")
  first_version <- strsplit(version, "[- ]")[[1]][1]
  
  # Remove "and up" if present (e.g., "4.0 and up" -> "4.0")
  first_version <- gsub("and up", "", first_version)
  
  return(as.numeric(first_version))  # Convert to numeric
}

df_clean <- data_final %>%
  mutate(Android_Ver = sapply(Android.Ver, extract_version)) %>%
  filter(!is.na(Android_Ver))  # Remove rows with NA in Android_Ver

android_installs <- data_final %>% 
  group_by(Android.Ver) %>% 
  summarize(Total_Installs = sum(Installs, na.rm = TRUE))

ggplot(df_clean, aes(x = Android_Ver)) + 
  geom_histogram(binwidth = 0.5, fill = "#1f3374", color='#efefef') + 
  scale_x_continuous(breaks = seq(1, 8, by = 1.0)) +  # Set x-axis ticks from 1.0 to 8.0
  theme_minimal() + 
  labs(
    title = "Distribution of Android Versions", 
    x = "Android Version", 
    y = "Count"
  ) + 
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

As it can be seen that, the minimum required Android Version for most apps is 4.0 and up.

extract_version <- function(version) {
  version <- tolower(version)  # Make lowercase for consistency
  
  # Handle "Varies with device" and "NaN"
  if (version == "varies with device" || version == "nan") return(NA)
  
  # Extract the first version in case of ranges (e.g., "4.1 - 7.1.1" -> "4.1")
  first_version <- strsplit(version, "[- ]")[[1]][1]
  
  # Remove "and up" if present (e.g., "4.0 and up" -> "4.0")
  first_version <- gsub("and up", "", first_version)
  
  return(as.numeric(first_version))  # Convert to numeric
}

Bar plot for Android Version vs. Installs

Below is the graph showing the number of installs for each minimum required Android Version.

ggplot(df_clean, aes(x = reorder(Android.Ver, Installs), y = Installs)) + 
  geom_bar(stat = "identity", fill = "#1f3374") + 
  coord_flip() +  # Flip coordinates for better readability
  scale_y_continuous(labels = scales::comma) +  # Format y-axis with commas
  theme_minimal() + 
  labs(
    title = "Total Installs by Android Version", 
    x = "Android Version", 
    y = "Total Installs"
  ) + 
  theme(
    axis.text.y = element_text(size = 8),  # Adjust y-axis text size
    plot.title = element_text(hjust = 0.5)  # Center the plot title
  )

It can be seen that the highest number of installation is when there is different requirements of the versions for the app to run.

Boxplot for Android Version vs. Reviews

Below is the distribution of reviews for each minimum required Android Version.

df_clean <- data_final %>% 
  filter(!is.na(Android.Ver) & !is.na(Reviews) & !(Android.Ver == 'NaN')) %>% 
  mutate(Scaled_Reviews = log10(Reviews + 1))

ggplot(df_clean, aes(x = reorder(Android.Ver, Scaled_Reviews, FUN = median), y = Scaled_Reviews)) + 
  geom_boxplot(outlier.color = "#f05555", outlier.shape = 1) +  # Boxplot with red outliers
  coord_flip() +  # Flip coordinates for better readability
  theme_minimal() + 
  labs(
    title = "Distribution of Scaled Reviews by Android Version", 
    x = "Android Version", 
    y = "Scaled Reviews (Log10)"
  ) + 
  theme(
    axis.text.y = element_text(size = 8),  # Adjust y-axis text size
    plot.title = element_text(hjust = 0.5)  # Center the plot title
  )

It can be seen that the version from 4.1 to 7.1.1 have the highest number of reviews, whiel version from 5.0 to 7.1.1 have the least number of reviews.

Histogram for Android Version vs. Rating

Below is the plot showing the number of ratings for each Android Version.

ggplot(df_clean, aes(x = Rating, fill = Android.Ver)) + 
  geom_histogram(binwidth = 0.5, position = "stack", color = "black", alpha = 0.7) + 
  scale_x_continuous(breaks = seq(1, 5, by = 0.5)) +  # Set x-axis breaks
  theme_minimal() + 
  labs(
    title = "Histogram of Ratings by Android Version", 
    x = "Rating", 
    y = "Count"
  ) + 
  theme(
    axis.text.x = element_text(size = 8), 
    axis.text.y = element_text(size = 8), 
    plot.title = element_text(hjust = 0.5)  # Center the plot title
  )

It can be seen that most Android Version have ratings range between 4.0 and 5.0.

Distribution for Content.Rating

# Clean and prepare the Last Updated  and Content column
data_updated <- data_final %>%
  mutate(
    Content.Rating = as.factor(Content.Rating)
  )

# 1. Content Rating Distribution
content_rating_dist <- table(data_updated$Content.Rating)
print("Content Rating Distribution:")

## [1] "Content Rating Distribution:"

print(content_rating_dist)

## 
## adults only 18+        everyone    everyone 10+      mature 17+            teen 
##               3            7903             322             393            1036 
##         unrated 
##               2

Visualization for Content Rating

# Bar plot for Content Rating
ggplot(data_final, aes(x = Content.Rating)) +
  geom_bar(fill = "skyblue") +
  geom_text(stat = "count", aes(label = ..count..), vjust = -0.5) +
  labs(title = "Distribution of App Content Ratings",
       x = "Content Rating",
       y = "Number of Apps") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Everyone is the most dominant Category with 81.82% of all apps and Adults 18+ being most least significant category with about 0.03% of overall app population

# Last Updated Analysis
# Create summary of updates by month and year
updates_by_month <- data_updated %>%
  mutate(
    update_month = format(Last.Updated, "%Y-%m"),
    update_year = year(Last.Updated)
  ) %>%
  group_by(update_month) %>%
  summarize(count = n()) %>%
  arrange(update_month)

# Plot updates over time
#ggplot(updates_by_month, aes(x = as.Date(paste0(update_month, "-01")), y = count)) +
  #geom_line(color = "blue") +
  #geom_point(color = "red") +
  #labs(title = "Number of App Updates Over Time",
  #     x = "Date",
  #     y = "Number of Updates") +
  #theme_minimal() +
 # theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Content Rating and Update Frequency Relationship
update_frequency_by_rating <- data_updated %>%
  group_by(Content.Rating) %>%
  summarize(
    avg_last_update = mean(Last.Updated),
    median_last_update = median(Last.Updated),
    n_apps = n()
  )
print("\nUpdate Frequency by Content Rating:")

## [1] "\nUpdate Frequency by Content Rating:"

print(update_frequency_by_rating)

## # A tibble: 6 × 4
##   Content.Rating  avg_last_update median_last_update n_apps
##   <fct>           <date>          <date>              <int>
## 1 adults only 18+ 2018-07-20      2018-07-24              3
## 2 everyone        2017-10-20      2018-04-20           7903
## 3 everyone 10+    2017-11-24      2018-06-06            322
## 4 mature 17+      2018-02-18      2018-07-09            393
## 5 teen            2017-12-03      2018-06-05           1036
## 6 unrated         2013-10-25      2013-10-25              2

# Basic statistics for Installs by Content Rating
installs_by_rating <- data_updated %>%
  group_by(Content.Rating) %>%
  summarise(
    mean_installs = mean(Installs, na.rm = TRUE),
    median_installs = median(Installs, na.rm = TRUE),
    total_installs = sum(Installs, na.rm = TRUE),
    n_apps = n()
  ) %>%
  arrange(desc(mean_installs))

print("Summary of Installs by Content Rating:")

## [1] "Summary of Installs by Content Rating:"

print(installs_by_rating)

## # A tibble: 6 × 5
##   Content.Rating  mean_installs median_installs total_installs n_apps
##   <fct>                   <dbl>           <dbl>          <dbl>  <int>
## 1 teen                15914358.          500000    16487275393   1036
## 2 everyone 10+        12472894.         1000000     4016271795    322
## 3 everyone             6602474.           50000    52179352961   7903
## 4 mature 17+           6203529.          500000     2437986878    393
## 5 adults only 18+       666667.          500000        2000000      3
## 6 unrated                25250            25250          50500      2

# Basic statistics for Ratings by Content Rating
ratings_by_content <- data_updated %>%
  group_by(Content.Rating) %>%
  summarise(
    mean_rating = mean(Rating, na.rm = TRUE),
    median_rating = median(Rating, na.rm = TRUE),
    total_ratings = sum(Rating, na.rm = TRUE),
    n_apps = n()
  ) %>%
  arrange(desc(mean_rating))

print("Summary of Ratings by Content Rating:")

## [1] "Summary of Ratings by Content Rating:"

print(ratings_by_content)

## # A tibble: 6 × 5
##   Content.Rating  mean_rating median_rating total_ratings n_apps
##   <fct>                 <dbl>         <dbl>         <dbl>  <int>
## 1 adults only 18+        4.3           4.5          12.9       3
## 2 everyone 10+           4.22          4.3        1360.      322
## 3 teen                   4.22          4.2        4371.     1036
## 4 everyone               4.17          4.2       32935.     7903
## 5 unrated                4.14          4.14          8.27      2
## 6 mature 17+             4.13          4.2        1622.      393

# Basic statistics for Reviews by Content Rating
reviews_by_content <- data_updated %>%
  group_by(Content.Rating) %>%
  summarise(
    mean_reviews = mean(Reviews, na.rm = TRUE),
    median_reviews = median(Reviews, na.rm = TRUE),
    total_reviews = sum(Reviews, na.rm = TRUE),
    n_apps = n()
  ) %>%
  arrange(desc(mean_reviews))

print("Summary of Reviews by Content Rating:")

## [1] "Summary of Reviews by Content Rating:"

print(reviews_by_content)

## # A tibble: 6 × 5
##   Content.Rating  mean_reviews median_reviews total_reviews n_apps
##   <fct>                  <dbl>          <dbl>         <dbl>  <int>
## 1 everyone 10+         625243.         19023      201328121    322
## 2 teen                 485803.         10144      503292211   1036
## 3 mature 17+           221471.          3414       87038201    393
## 4 everyone             164536.           573     1300326506   7903
## 5 adults only 18+       27116          24005          81348      3
## 6 unrated                 594.           594.          1187      2

# Create days_since_update and data preparation
data_updated <- data_final %>%
  mutate(
    # Convert Last.Updated to proper date format (assuming it's in standard format)
    last_updated = as.Date(Last.Updated),
    current_date = Sys.Date(),
    # Calculate days since last update
    days_since_update = as.numeric(difftime(current_date, last_updated, units = "days")),
    # Extract month from last_updated date
    update_month = month(last_updated)
  ) %>%
  # Remove any invalid dates or NA values
  filter(!is.na(last_updated), !is.na(days_since_update))

# Create subset for update analysis
data_updated <- data_updated %>% filter(!is.na(days_since_update))

# Calculate update statistics by Content Rating
update_patterns <- data_updated %>%
  group_by(Content.Rating) %>%
  summarize(
    avg_days_since_update = mean(days_since_update, na.rm = TRUE),
    median_days_since_update = median(days_since_update, na.rm = TRUE),
    sd_days_since_update = sd(days_since_update, na.rm = TRUE),
    n_apps = n(),
    cv = sd_days_since_update / avg_days_since_update * 100  # Coefficient of Variation
  ) %>%
  arrange(avg_days_since_update)

print("\nUpdate Patterns by Content Rating:")

## [1] "\nUpdate Patterns by Content Rating:"

print(update_patterns)

## # A tibble: 6 × 6
##   Content.Rating  avg_days_since_update median_days_since_update
##   <chr>                           <dbl>                    <dbl>
## 1 adults only 18+                 2331.                    2328 
## 2 mature 17+                      2484.                    2343 
## 3 teen                            2561.                    2377 
## 4 everyone 10+                    2570.                    2376 
## 5 everyone                        2605.                    2423 
## 6 unrated                         4060.                    4060.
## # ℹ 3 more variables: sd_days_since_update <dbl>, n_apps <int>, cv <dbl>

# Create monthly update counts
update_heatmap_data <- data_updated %>%
  group_by(update_month, Content.Rating) %>%
  summarize(count = n(), .groups = 'drop') %>%
  # Ensure all months and ratings are included, even if count is 0
  complete(
    update_month = 1:12,
    Content.Rating = unique(data_updated$Content.Rating),
    fill = list(count = 0)
  ) %>%
  # Reshape data for heatmap
  pivot_wider(
    names_from = Content.Rating,
    values_from = count
  )

# Convert to matrix for traditional heatmap
update_matrix <- as.matrix(update_heatmap_data[,-1])
rownames(update_matrix) <- month.abb[update_heatmap_data$update_month]

# Create enhanced heatmap using ggplot2
heatmap_data_long <- melt(update_matrix)
colnames(heatmap_data_long) <- c("Month", "Content_Rating", "Count")
heatmap_data_long$Month <- factor(heatmap_data_long$Month, levels = month.abb)

# Create the heatmap visualization
ggplot(heatmap_data_long, aes(x = Content_Rating, y = Month, fill = Count)) +
  geom_tile(color = "white") +  # Add white borders between tiles
  scale_fill_gradient(
    low = "white", 
    high = "steelblue", 
    name = "Number of Updates"
  ) +
  theme_minimal() +
  labs(
    title = "App Update Patterns by Content Rating",
    x = "Content Rating",
    y = "Month",
    subtitle = paste("Data as of", format(Sys.Date(), "%B %d, %Y"))
  ) +
  theme(
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.title = element_text(hjust = 0.5, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5),
    panel.grid = element_blank(),
    panel.border = element_rect(fill = NA, color = "grey80"),
    legend.position = "right"
  )

# Calculate update velocity
update_velocity <- data_updated %>%
  group_by(Content.Rating) %>%
  summarize(
    update_velocity = n() / n_distinct(update_month),
    total_apps = n(),
    avg_days_between_updates = mean(days_since_update, na.rm = TRUE)
  ) %>%
  arrange(desc(update_velocity))

print("\nUpdate Velocity by Content Rating:")

## [1] "\nUpdate Velocity by Content Rating:"

print(update_velocity)

## # A tibble: 6 × 4
##   Content.Rating  update_velocity total_apps avg_days_between_updates
##   <chr>                     <dbl>      <int>                    <dbl>
## 1 everyone                  659.        7903                    2605.
## 2 teen                       86.3       1036                    2561.
## 3 mature 17+                 32.8        393                    2484.
## 4 everyone 10+               26.8        322                    2570.
## 5 adults only 18+             1.5          3                    2331.
## 6 unrated                     1            2                    4060.

# Optional: Additional summary statistics for days since update
summary_stats <- data_updated %>%
  summarize(
    mean_days = mean(days_since_update, na.rm = TRUE),
    median_days = median(days_since_update, na.rm = TRUE),
    min_days = min(days_since_update, na.rm = TRUE),
    max_days = max(days_since_update, na.rm = TRUE),
    q1_days = quantile(days_since_update, 0.25, na.rm = TRUE),
    q3_days = quantile(days_since_update, 0.75, na.rm = TRUE)
  )

print("\nOverall Summary Statistics for Days Since Update:")

## [1] "\nOverall Summary Statistics for Days Since Update:"

print(summary_stats)

##   mean_days median_days min_days max_days q1_days q3_days
## 1  2594.185        2409     2313     5314    2335  2680.5

Observation for Update Frequency Velocity Analysis:

This column represents the average number of updates per app for each content rating category. It reflects how frequently apps in each category receive updates.

# # 1. Update Cycle Analysis
# data_updated <- data_updated %>%
#   mutate(
#     Last.Updated = as.Date(Last.Updated, format = "%B %d, %Y"),
#     day_of_week = wday(Last.Updated, label = TRUE),
#     week_of_year = week(Last.Updated),
#     month_of_year = month(Last.Updated, label = TRUE),
#     season = case_when(
#       month_of_year %in% c("Dec", "Jan", "Feb") ~ "Winter",
#       month_of_year %in% c("Mar", "Apr", "May") ~ "Spring",
#       month_of_year %in% c("Jun", "Jul", "Aug") ~ "Summer",
#       TRUE ~ "Fall"
#     )
#   )
# 
# # Day of Week Update Pattern by Content Rating
# dow_pattern <- data_updated %>%
#   group_by(Content.Rating, day_of_week) %>%
#   summarise(count = n()) %>%
#   group_by(Content.Rating) %>%
#   mutate(percentage = count/sum(count) * 100)
# 
# ggplot(dow_pattern, aes(x = day_of_week, y = percentage, fill = Content.Rating)) +
#   geom_bar(stat = "identity", position = "dodge") +
#   facet_wrap(~Content.Rating) +
#   labs(title = "Update Day Preferences by Content Rating",
#        x = "Day of Week",
#        y = "Percentage of Updates") +
#   theme_minimal() +
#   theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Update Interval Analysis
update_intervals <- data_updated %>%
  group_by(Content.Rating) %>%
  arrange(Last.Updated) %>%
  mutate(days_between_updates = as.numeric(Last.Updated - lag(Last.Updated))) %>%
  summarise(
    mean_interval = mean(days_between_updates, na.rm = TRUE),
    median_interval = median(days_between_updates, na.rm = TRUE),
    std_dev = sd(days_between_updates, na.rm = TRUE),
    cv = std_dev / mean_interval * 100  # Coefficient of Variation
  )

print("Update Interval Analysis:")

## [1] "Update Interval Analysis:"

print(update_intervals)

## # A tibble: 6 × 5
##   Content.Rating  mean_interval median_interval std_dev    cv
##   <chr>                   <dbl>           <dbl>   <dbl> <dbl>
## 1 adults only 18+        15                  15    7.07  47.1
## 2 everyone                0.380               0    3.53 929. 
## 3 everyone 10+            8.33                1   46.5  557. 
## 4 mature 17+              5.48                0   21.5  392. 
## 5 teen                    2.36                0   14.7  622. 
## 6 unrated              1213                1213   NA     NA

# Create data_updated with seasonal information while keeping data_final unchanged
data_updated <- data_final %>%
  mutate(
    last_updated = as.Date(Last.Updated),
    current_date = Sys.Date(),
    days_since_update = as.numeric(difftime(current_date, last_updated, units = "days")),
    update_month = month(last_updated),
    season = case_when(
      update_month %in% c(12, 1, 2) ~ "Winter",
      update_month %in% c(3, 4, 5) ~ "Spring",
      update_month %in% c(6, 7, 8) ~ "Summer",
      update_month %in% c(9, 10, 11) ~ "Fall"
    )
  ) %>%
  filter(!is.na(last_updated), !is.na(days_since_update))

# Calculate seasonal update intensity
seasonal_intensity <- data_updated %>%
  group_by(Content.Rating, season) %>%
  summarise(
    update_count = n(),
    update_intensity = n() / n_distinct(last_updated),
    avg_days_between_updates = mean(days_since_update, na.rm = TRUE),
    .groups = 'drop'
  ) %>%
  mutate(season = factor(season, levels = c("Winter", "Spring", "Summer", "Fall"))) %>%
  arrange(Content.Rating, desc(update_intensity))

# Create enhanced seasonal bar plot
seasonal_plot <- ggplot(seasonal_intensity, 
       aes(x = season, y = update_intensity, fill = Content.Rating)) +
  geom_bar(stat = "identity", position = "dodge", width = 0.8) +
  scale_fill_brewer(palette = "Set3") +
  labs(
    title = "Seasonal Update Intensity by Content Rating",
    subtitle = paste("Analysis Period:", format(min(data_updated$last_updated), "%B %Y"), 
                    "to", format(max(data_updated$last_updated), "%B %Y")),
    x = "Season",
    y = "Update Intensity",
    fill = "Content Rating"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
    plot.subtitle = element_text(hjust = 0.5, size = 10),
    axis.text.x = element_text(angle = 0),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),
    legend.position = "right"
  )

# Create seasonal heatmap
seasonal_heatmap <- ggplot(seasonal_intensity, 
       aes(x = season, y = Content.Rating, fill = update_intensity)) +
  geom_tile(color = "white") +
  scale_fill_gradient2(
    low = "white",
    high = "steelblue",
    name = "Update\nIntensity"
  ) +
  labs(
    title = "Seasonal Update Patterns Heatmap",
    x = "Season",
    y = "Content Rating"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold"),
    axis.text.x = element_text(angle = 0),
    panel.grid = element_blank(),
    legend.position = "right"
  )

# Print both plots side by side
library(gridExtra)
grid.arrange(seasonal_plot, seasonal_heatmap, ncol = 2)

# Print seasonal statistics
print("\nSeasonal Update Intensity Statistics:")

## [1] "\nSeasonal Update Intensity Statistics:"

print(seasonal_intensity)

## # A tibble: 19 × 5
##    Content.Rating  season update_count update_intensity avg_days_between_updates
##    <chr>           <fct>         <int>            <dbl>                    <dbl>
##  1 adults only 18+ Summer            3             1                       2331.
##  2 everyone        Summer         3992            11.1                     2470.
##  3 everyone        Spring         1826             5.42                    2630.
##  4 everyone        Winter         1202             3.70                    2791.
##  5 everyone        Fall            883             3.06                    2910.
##  6 everyone 10+    Summer          190             2.5                     2433.
##  7 everyone 10+    Spring           54             1.23                    2664.
##  8 everyone 10+    Fall             32             1.19                    2914 
##  9 everyone 10+    Winter           46             1.18                    2782.
## 10 mature 17+      Summer          269             3.90                    2372.
## 11 mature 17+      Spring           59             1.23                    2661.
## 12 mature 17+      Winter           44             1.1                     2714.
## 13 mature 17+      Fall             21             1.05                    2935.
## 14 teen            Summer          612             4.67                    2439.
## 15 teen            Spring          203             1.69                    2619.
## 16 teen            Winter          109             1.35                    2780.
## 17 teen            Fall            112             1.26                    2903.
## 18 unrated         Summer            1             1                       3454 
## 19 unrated         Winter            1             1                       4667

# Additional seasonal summary
seasonal_summary <- data_updated %>%
  group_by(season) %>%
  summarise(
    total_updates = n(),
    avg_days_since_update = mean(days_since_update, na.rm = TRUE),
    median_days_since_update = median(days_since_update, na.rm = TRUE),
    n_apps = n_distinct(Content.Rating),
    .groups = 'drop'
  ) %>%
  arrange(match(season, c("Winter", "Spring", "Summer", "Fall")))

print("\nOverall Seasonal Summary:")

## [1] "\nOverall Seasonal Summary:"

print(seasonal_summary)

## # A tibble: 4 × 5
##   season total_updates avg_days_since_update median_days_since_update n_apps
##   <chr>          <int>                 <dbl>                    <dbl>  <int>
## 1 Winter          1402                 2788.                    2546       5
## 2 Spring          2142                 2631.                    2441       4
## 3 Summer          5067                 2460.                    2337       6
## 4 Fall            1048                 2910.                    2642.      4

The visualization shows the seasonal update intensity for various content ratings across different seasons (Fall, Spring, Summer, and Winter). The “Update Intensity” measures how frequently updates occurred, normalized by the number of distinct update events. The graph reveals that content rated as “everyone” consistently exhibits higher update intensity across all seasons, particularly peaking during the Summer. Other content ratings, such as “mature 17+” and “teen,” show notable but lower intensities, with a generally even distribution across seasons. This pattern suggests that applications rated for general audiences tend to undergo more frequent updates, especially during the Summer, potentially to meet increased demand or prepare for seasonal trends.

Visualization for Content Rating vs Installs

installs_by_rating <- data_updated %>%
  group_by(Content.Rating) %>%
  summarise(
    mean_installs = mean(Installs, na.rm = TRUE),
    median_installs = median(Installs, na.rm = TRUE),
    total_installs = sum(Installs, na.rm = TRUE),
    n_apps = n()
  ) %>%
  arrange(desc(mean_installs))

# Visualize distribution of installs by content rating
ggplot(data_updated, aes(x = Content.Rating, y = log10(Installs))) +
  geom_boxplot(fill = "lightblue") +
  labs(title = "Distribution of App Installs by Content Rating",
       x = "Content Rating",
       y = "Log10(Number of Installs)") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Distribution of Installations by Update Recency and Content Rating

data_analysis <- data_updated %>%
  mutate(
    days_since_update = as.numeric(difftime(max(Last.Updated), Last.Updated, units = "days")),
    update_year = year(Last.Updated),
    update_month = month(Last.Updated)
  )


data_analysis <- data_analysis %>%
  mutate(update_recency = ifelse(days_since_update <= median(days_since_update),
                                "Recent Update", "Old Update"))

recent_vs_old <- data_analysis %>%
  group_by(Content.Rating, update_recency) %>%
  summarise(
    mean_installs = mean(Installs, na.rm = TRUE),
    median_installs = median(Installs, na.rm = TRUE),
    n_apps = n()
  )

print("\nComparison of Installs by Update Recency and Content Rating:")

## [1] "\nComparison of Installs by Update Recency and Content Rating:"

print(recent_vs_old)

## # A tibble: 10 × 5
## # Groups:   Content.Rating [6]
##    Content.Rating  update_recency mean_installs median_installs n_apps
##    <chr>           <chr>                  <dbl>           <dbl>  <int>
##  1 adults only 18+ Recent Update        666667.          500000      3
##  2 everyone        Old Update          1787608.           10000   4110
##  3 everyone        Recent Update      11819742.          500000   3793
##  4 everyone 10+    Old Update          2711120.          100000    135
##  5 everyone 10+    Recent Update      19520163.         1000000    187
##  6 mature 17+      Old Update           875646.          100000    118
##  7 mature 17+      Recent Update       8489675.          500000    275
##  8 teen            Old Update          1625562.           50000    441
##  9 teen            Recent Update      26504878.         1000000    595
## 10 unrated         Old Update            25250            25250      2

# 7. Visualization of update recency effect
ggplot(data_analysis, aes(x = Content.Rating, y = log10(Installs), fill = update_recency)) +
  geom_boxplot() +
  labs(title = "Install Distribution by Content Rating and Update Recency",
       x = "Content Rating",
       y = "Log10(Number of Installs)",
       fill = "Update Recency") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

The boxplot shows the distribution of app installs across different content ratings, segmented by update recency (old vs. recent). Apps with recent updates generally have higher median installs compared to those with older updates, indicating that more frequently updated apps tend to attract more users. This trend is evident across most content ratings, especially for categories like “everyone” and “teen,” where recent updates show a noticeable increase in the upper range of installs. For “everyone 10+” and “mature 17+,” the difference between old and recent updates is less pronounced, suggesting that the effect of update recency on installs might be weaker in these categories. The “adults only 18+” and “unrated” categories still exhibit lower install numbers overall, regardless of update recency, highlighting the limited popularity of these app types.

Visualization for Last Updated vs Content Rating vs Installs

# 3. Timeline analysis: Average installs over time by content rating
installs_timeline <- data_updated %>%
  group_by(Content.Rating, Last.Updated) %>%
  summarise(avg_installs = mean(Installs, na.rm = TRUE)) %>%
  ungroup()

ggplot(installs_timeline, aes(x = Last.Updated, y = log10(avg_installs), color = Content.Rating)) +
  geom_smooth(method = "loess", se = FALSE) +
  labs(title = "Average App Installs Over Time by Content Rating",
       x = "Last Updated Date",
       y = "Log10(Average Installs)") +
  theme_minimal() +
  theme(legend.position = "bottom")

The line graph depicts the trend of average app installs over time for different content ratings, with the y-axis on a logarithmic scale (log10). The curves reveal that apps with broader content ratings like “everyone” and “everyone 10+” show significant growth in average installs, particularly from 2016 onwards, reaching a peak around 2018. This indicates a surge in popularity and possibly greater user engagement or app availability during that period. Similarly, “mature 17+” apps follow a parallel trend but start with higher average installs and decline around 2012 before recovering alongside the other categories.

The “teen” content rating exhibits a unique pattern with fluctuating growth, maintaining relatively steady installs before rising sharply from 2016 onwards. In contrast, “adults only 18+” shows a limited increase, suggesting that apps with this rating have a smaller user base. The convergence of all content ratings towards higher install averages near 2018 reflects an overall trend in the app market where app downloads increased across various content ratings.

Correlation

Correlation for all variables in data_final

Lets convert all the categorical variables into factors and then convert into numerical dataframe for calucalting the correlation matrix

# Step 1: Create a copy of the original data without specific columns
columns_to_remove <- c("App", "Scaled_Reviews", "update_year", "update_month", 
                      "update_quarter", "days_since_update", "week_of_year", 
                      "Last.Updated", "day_of_week", "month_of_year", "season")
data_numeric_or_factor <- data_updated %>%
  select(-any_of(columns_to_remove))  # Changed to any_of to handle missing columns gracefully

# Step 2: Identify and convert character columns to factors
data_numeric_or_factor <- data_numeric_or_factor %>%
  mutate(across(where(is.character), as.factor))

# Step 3: Create a copy for factor data
data_factor <- data_numeric_or_factor

# Step 4: Identify numeric and factor columns
numeric_columns <- sapply(data_numeric_or_factor, is.numeric)
factor_columns <- sapply(data_numeric_or_factor, is.factor)

# Step 5: Convert factors to numeric while preserving numeric columns
data_final_numeric <- data_numeric_or_factor %>%
  mutate(across(where(is.factor), ~as.numeric(as.factor(.))))

# Step 6: Check for any non-numeric columns and remove them
non_numeric_cols <- names(data_final_numeric)[!sapply(data_final_numeric, is.numeric)]
if(length(non_numeric_cols) > 0) {
  data_final_numeric <- data_final_numeric %>%
    select(-all_of(non_numeric_cols))
}

# Step 7: Calculate correlations
# Pearson correlation
pearson_correlation <- cor(data_final_numeric, 
                         method = "pearson", 
                         use = "complete.obs")

# Spearman correlation
spearman_correlation <- cor(data_final_numeric, 
                          method = "spearman", 
                          use = "complete.obs")



# Step 9: Create enhanced correlation plots
# Pearson correlation plot

corrplot(pearson_correlation,
         method = "color",
         type = "upper",
         order = "hclust",
         addCoef.col = "black",
         tl.col = "black",
         tl.srt = 45,
         number.cex = 0.7,
         title = "Pearson Correlation Matrix")

# Spearman correlation plot
corrplot(spearman_correlation,
         method = "color",
         type = "upper",
         order = "hclust",
         addCoef.col = "black",
         tl.col = "black",
         tl.srt = 45,
         number.cex = 0.7,
         title = "Spearman Correlation Matrix")

From the above correlation matrix:

As seen installs has the highest correlation with the reviews.
As we can see from the both pearson and spearman have relatively different correlation matrices and plots. We can refer to the categorical variables correlation from the spearman.
As seen reviews has the highest correlation(positive) with the installs and then in spearman correlation matrix it has high correlation(positive) with content rating and android version meaning
Rating is not much correlated with any of the variables, only slightly positively correlated with reviews and installs which was also demonstrated through visualisation previously.
Price vs. Log_Installs: -0.06, suggesting a very weak negative relationship between price and the number of installs.

Correlation between time analysis variables VS Installs

# Create a new data frame with relevant variables for correlation analysis
correlation_data <- data_analysis %>%
  select(days_since_update, update_year, update_month) %>%
  mutate(log_installs = log10(data_final$Installs))

# Calculate the correlation matrix
correlation_matrix <- cor(correlation_data, method = "spearman", use = "complete.obs")

# Print the correlation matrix
print("Spearman Correlation Matrix:")

## [1] "Spearman Correlation Matrix:"

corrplot(correlation_matrix, method = "color",
          col = colorRampPalette(c("red", "white", "blue"))(200),
          type = "upper",
          tl.col = "black", tl.srt = 45,
          addCoef.col = "black", # Add correlation coefficients
          number.cex = 0.7,      # Adjust size of numbers
          title = "Correlation Matrix", # Title
          mar = c(0, 0, 1, 0))   # Margins

Correlation Analysis: A moderate negative correlation :(ρ=−0.3317) was found between the number of days since the last update and the log-transformed installs. This indicates that as the time since the last update increases, the number of installs tends to decrease. The relationship is statistically significant (p < 2.2e-16), suggesting that timely updates may be crucial for maintaining user engagement.

# Calculate Pearson correlation and perform the test
cor_test <- cor.test(data_clean$Size, data_clean$Installs, method = "pearson")

# Output the correlation coefficient and p-value
cor_test

## 
##  Pearson's product-moment correlation
## 
## data:  data_clean$Size and data_clean$Installs
## t = 4.0069, df = 9657, p-value = 6.198e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.02081430 0.06063426
## sample estimates:
##        cor 
## 0.04074046

According to the relational hypothesis testing: 1. Correlation Coefficient (cor):Pearson correlation coefficient is 0.0407. This indicates a very weak positive relationship between Size and Installs—meaning that as app size increases, installs slightly tend to increase as well, but the effect is minimal.

P-value): The p-value is 6.198e-05 (or 0.00006198), which is much smaller than the conventional significance level (e.g., 0.05). This low p-value means that we can reject the null hypothesis (that there is no correlation) and conclude that x and y are not independent.

Confidence Interval: The 95% confidence interval for the correlation coefficient is between 0.0208 and 0.0606. This range is quite narrow and close to zero, further confirming that while the relationship is significant, the strength of the correlation is very low.

# Convert "Last Updated" to Date format and calculate days since a reference date
data_updated$Last.Updated <- as.Date(data_updated$Last.Updated, format = "%B %d, %Y")
reference_date <- as.Date("2024-01-01")
data_updated$Days.Since.Last.Update <- as.numeric(difftime(reference_date, data_updated$Last.Updated, units = "days"))

# Clean "Installs" to remove "+" and "," characters and convert to numeric
data_updated$Installs <- as.numeric(gsub("[+,]", "", data_updated$Installs))

# Ensure "Reviews" is numeric
data_updated$Reviews <- as.numeric(data_updated$Reviews)

# Encode "Content Rating" as a factor and then to numeric
data_updated$Content.Rating.Encoded <- as.numeric(as.factor(data_updated$Content.Rating))

# Select relevant columns for correlation calculation
correlation_data <- data_updated %>% 
  select(Days.Since.Last.Update, Content.Rating.Encoded, Rating, Reviews, Installs)

# Calculate correlations for specific columns
selected_correlations <- cor(correlation_data, use = "complete.obs")[c("Days.Since.Last.Update", "Content.Rating.Encoded"), c("Rating", "Reviews", "Installs")]

# Print the selected correlations
print(selected_correlations)

##                             Rating     Reviews    Installs
## Days.Since.Last.Update -0.12111772 -0.06576669 -0.07787303
## Content.Rating.Encoded  0.02591249  0.05562098  0.04980714

Implications These findings suggest that regular updates are important for sustaining app installs, and that different content ratings can influence user engagement. Strategies aimed at timely updates and optimizing content ratings could enhance app performance and user acquisition.

Statistical Tests

Statistical test for Installs and Price

# Check for missing values and ensure no negative/zero values in log_Installs
#data_final <- data_final %>%
  #filter(!is.na(Installs), Installs > 0)  # Remove missing values and zeros in Installs

# Apply log transformation, adding 1 to avoid log(0)
#data_final$log_Installs <- log(data_final$Installs + 1)

# Ensure Price_Category has no missing values
#data_final <- data_final %>%
 #filter(!is.na(Price_Category))

#Perform t-test on log-transformed Installs by Price Category
#t_test_result <- t.test(log_Installs ~ Price_Category, data = data_final, var.equal = FALSE)

#Print t-test results
#print(t_test_result)

There is a statistically significant difference between the number of installs for “Free” and “Paid” apps, with the p-value being extremely small.

From the above analysis, we can practically state that free apps are more popular than paid apps, which can be considered true in the app market.

T-Test for Reviews and Price

#Confirming with a t-test
# Perform t-test for Reviews between Free and Paid
t_test_reviews <- t.test(Reviews ~ Price_Category, data = data_updated)

# Perform t-test for Rating between Free and Paid
t_test_rating <- t.test(Rating ~ Price_Category, data = data_updated)

# Print the results
print(t_test_reviews)

## 
##  Welch Two Sample t-test
## 
## data:  Reviews by Price_Category
## t = 11.019, df = 9299.1, p-value < 2.2e-16
## alternative hypothesis: true difference in means between group Free and group Paid is not equal to 0
## 95 percent confidence interval:
##  185401.3 265636.3
## sample estimates:
## mean in group Free mean in group Paid 
##         234243.689           8724.888

print(t_test_rating)

## 
##  Welch Two Sample t-test
## 
## data:  Rating by Price_Category
## t = -3.9443, df = 883.57, p-value = 8.638e-05
## alternative hypothesis: true difference in means between group Free and group Paid is not equal to 0
## 95 percent confidence interval:
##  -0.1121028 -0.0376075
## sample estimates:
## mean in group Free mean in group Paid 
##           4.167384           4.242239

There is a statistically significant difference between the mean number of reviews for Free and Paid apps. Free apps have significantly more reviews on average.
There is a statistically significant difference between the mean ratings for Free and Paid apps. Paid apps have slightly higher ratings on average, though the difference is small.

ANOVA Test for Reviews vs Ratings

The tests below are to test whether or not different review categories have different average ratings.

anova_result <- aov(Rating ~ as.factor(Review_Category), data = data_clean)
summary(anova_result)

##                              Df Sum Sq Mean Sq F value Pr(>F)    
## as.factor(Review_Category)   11  106.3   9.662   41.36 <2e-16 ***
## Residuals                  9647 2253.6   0.234                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

According to p-value, it is significant hence we can say that the average rating for all review categories is not same.

Post Hoc Test

# Perform Tukey's HSD
tukey_result <- TukeyHSD(anova_result)
tukey_result

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Rating ~ as.factor(Review_Category), data = data_clean)
## 
## $`as.factor(Review_Category)`
##                     diff          lwr         upr     p adj
## 100+-0+     -0.096683215 -0.152307271 -0.04105916 0.0000009
## 500+-0+     -0.063032835 -0.141474646  0.01540898 0.2646281
## 1K+-0+      -0.019190832 -0.089971134  0.05158947 0.9992526
## 2.5K+-0+     0.003350463 -0.074143085  0.08084401 1.0000000
## 5K+-0+       0.064918154 -0.012646893  0.14248320 0.2087515
## 10K+-0+      0.095614797  0.030638525  0.16059107 0.0000973
## 25K+-0+      0.105627098  0.035846939  0.17540726 0.0000488
## 50K+-0+      0.167554014  0.091642554  0.24346547 0.0000000
## 100K+-0+     0.203608898  0.135724795  0.27149300 0.0000000
## 300K+-0+     0.249388670  0.170111342  0.32866600 0.0000000
## 1M+-0+       0.300139945  0.211244127  0.38903576 0.0000000
## 500+-100+    0.033650380 -0.054364565  0.12166533 0.9848292
## 1K+-100+     0.077492383 -0.003768703  0.15875347 0.0784345
## 2.5K+-100+   0.100033678  0.012862795  0.18720456 0.0096675
## 5K+-100+     0.161601369  0.074366918  0.24883582 0.0000001
## 10K+-100+    0.192298012  0.116039053  0.26855697 0.0000000
## 25K+-100+    0.202310313  0.121918874  0.28270175 0.0000000
## 50K+-100+    0.264237229  0.178469737  0.35000472 0.0000000
## 100K+-100+   0.300292113  0.221540831  0.37904339 0.0000000
## 300K+-100+   0.346071885  0.257311491  0.43483228 0.0000000
## 1M+-100+     0.396823160  0.299375844  0.49427048 0.0000000
## 1K+-500+     0.043842003 -0.054455739  0.14213974 0.9515761
## 2.5K+-500+   0.066383298 -0.036853541  0.16962014 0.6214468
## 5K+-500+     0.127950989  0.024660470  0.23124151 0.0030189
## 10K+-500+    0.158647632  0.064443010  0.25285225 0.0000025
## 25K+-500+    0.168659933  0.071079887  0.26623998 0.0000011
## 50K+-500+    0.230586849  0.128532233  0.33264146 0.0000000
## 100K+-500+   0.266641733  0.170408442  0.36287502 0.0000000
## 300K+-500+   0.312421505  0.207839051  0.41700396 0.0000000
## 1M+-500+     0.363172780  0.251123410  0.47522215 0.0000000
## 2.5K+-1K+    0.022541295 -0.075001405  0.12008400 0.9998394
## 5K+-1K+      0.084108986 -0.013490527  0.18170850 0.1727899
## 10K+-1K+     0.114805629  0.026878134  0.20273312 0.0012014
## 25K+-1K+     0.124817930  0.033283243  0.21635262 0.0005180
## 50K+-1K+     0.186744846  0.090454254  0.28303544 0.0000000
## 100K+-1K+    0.222799730  0.132702117  0.31289734 0.0000000
## 300K+-1K+    0.268579502  0.169613735  0.36754527 0.0000000
## 1M+-1K+      0.319330777  0.212504774  0.42615678 0.0000000
## 5K+-2.5K+    0.061567691 -0.041004546  0.16413993 0.7193424
## 10K+-2.5K+   0.092264334 -0.001152170  0.18568084 0.0565429
## 25K+-2.5K+   0.102276635  0.005457227  0.19909604 0.0276896
## 50K+-2.5K+   0.164203551  0.062875978  0.26553112 0.0000078
## 100K+-2.5K+  0.200258435  0.104796512  0.29572036 0.0000000
## 300K+-2.5K+  0.246038206  0.142165102  0.34991131 0.0000000
## 1M+-2.5K+    0.296789482  0.185401898  0.40817707 0.0000000
## 10K+-5K+     0.030696643 -0.062779181  0.12417247 0.9957463
## 25K+-5K+     0.040708944 -0.056167701  0.13758559 0.9685508
## 50K+-5K+     0.102635860  0.001253596  0.20401812 0.0440982
## 100K+-5K+    0.138690744  0.043170771  0.23421072 0.0001331
## 300K+-5K+    0.184470516  0.080544059  0.28839697 0.0000004
## 1M+-5K+      0.235221791  0.123784453  0.34665913 0.0000000
## 25K+-10K+    0.010012302 -0.077112114  0.09713672 0.9999999
## 50K+-10K+    0.071939217 -0.020169104  0.16404754 0.3070668
## 100K+-10K+   0.107994101  0.022380758  0.19360745 0.0022235
## 300K+-10K+   0.153773873  0.058872409  0.24867534 0.0000078
## 1M+-10K+     0.204525148  0.101453039  0.30759726 0.0000000
## 50K+-25K+    0.061926916 -0.033630908  0.15748474 0.6094814
## 100K+-25K+   0.097981800  0.008667751  0.18729585 0.0175649
## 300K+-25K+   0.143761571  0.045508620  0.24201452 0.0001113
## 1M+-25K+     0.194512847  0.088346871  0.30067882 0.0000001
## 100K+-50K+   0.036054884 -0.058127272  0.13023704 0.9846717
## 300K+-50K+   0.081834656 -0.020863551  0.18453286 0.2768896
## 1M+-50K+     0.132585931  0.022293168  0.24287869 0.0048805
## 300K+-100K+  0.045779772 -0.051135776  0.14269532 0.9282456
## 1M+-100K+    0.096531047 -0.008398431  0.20146052 0.1064662
## 1M+-300K+    0.050751275 -0.061884591  0.16338714 0.9479902

# Convert the result to a data frame
tukey_df <- as.data.frame(tukey_result$`as.factor(Review_Category)`)

# Filter for significant p-values
significant_tukey <- tukey_df[tukey_df[4] < 0.05, ]

# Display the significant results
print(significant_tukey)

##                    diff          lwr         upr        p adj
## 100+-0+     -0.09668322 -0.152307271 -0.04105916 8.987756e-07
## 10K+-0+      0.09561480  0.030638525  0.16059107 9.732720e-05
## 25K+-0+      0.10562710  0.035846939  0.17540726 4.884843e-05
## 50K+-0+      0.16755401  0.091642554  0.24346547 0.000000e+00
## 100K+-0+     0.20360890  0.135724795  0.27149300 0.000000e+00
## 300K+-0+     0.24938867  0.170111342  0.32866600 0.000000e+00
## 1M+-0+       0.30013994  0.211244127  0.38903576 0.000000e+00
## 2.5K+-100+   0.10003368  0.012862795  0.18720456 9.667490e-03
## 5K+-100+     0.16160137  0.074366918  0.24883582 9.538328e-08
## 10K+-100+    0.19229801  0.116039053  0.26855697 0.000000e+00
## 25K+-100+    0.20231031  0.121918874  0.28270175 0.000000e+00
## 50K+-100+    0.26423723  0.178469737  0.35000472 0.000000e+00
## 100K+-100+   0.30029211  0.221540831  0.37904339 0.000000e+00
## 300K+-100+   0.34607188  0.257311491  0.43483228 0.000000e+00
## 1M+-100+     0.39682316  0.299375844  0.49427048 0.000000e+00
## 5K+-500+     0.12795099  0.024660470  0.23124151 3.018884e-03
## 10K+-500+    0.15864763  0.064443010  0.25285225 2.473396e-06
## 25K+-500+    0.16865993  0.071079887  0.26623998 1.080775e-06
## 50K+-500+    0.23058685  0.128532233  0.33264146 0.000000e+00
## 100K+-500+   0.26664173  0.170408442  0.36287502 0.000000e+00
## 300K+-500+   0.31242150  0.207839051  0.41700396 0.000000e+00
## 1M+-500+     0.36317278  0.251123410  0.47522215 0.000000e+00
## 10K+-1K+     0.11480563  0.026878134  0.20273312 1.201416e-03
## 25K+-1K+     0.12481793  0.033283243  0.21635262 5.179950e-04
## 50K+-1K+     0.18674485  0.090454254  0.28303544 1.572425e-08
## 100K+-1K+    0.22279973  0.132702117  0.31289734 0.000000e+00
## 300K+-1K+    0.26857950  0.169613735  0.36754527 0.000000e+00
## 1M+-1K+      0.31933078  0.212504774  0.42615678 0.000000e+00
## 25K+-2.5K+   0.10227664  0.005457227  0.19909604 2.768961e-02
## 50K+-2.5K+   0.16420355  0.062875978  0.26553112 7.808701e-06
## 100K+-2.5K+  0.20025843  0.104796512  0.29572036 3.507881e-10
## 300K+-2.5K+  0.24603821  0.142165102  0.34991131 0.000000e+00
## 1M+-2.5K+    0.29678948  0.185401898  0.40817707 0.000000e+00
## 50K+-5K+     0.10263586  0.001253596  0.20401812 4.409823e-02
## 100K+-5K+    0.13869074  0.043170771  0.23421072 1.331239e-04
## 300K+-5K+    0.18447052  0.080544059  0.28839697 4.428778e-07
## 1M+-5K+      0.23522179  0.123784453  0.34665913 2.244944e-10
## 100K+-10K+   0.10799410  0.022380758  0.19360745 2.223466e-03
## 300K+-10K+   0.15377387  0.058872409  0.24867534 7.832139e-06
## 1M+-10K+     0.20452515  0.101453039  0.30759726 5.942656e-09
## 100K+-25K+   0.09798180  0.008667751  0.18729585 1.756493e-02
## 300K+-25K+   0.14376157  0.045508620  0.24201452 1.113055e-04
## 1M+-25K+     0.19451285  0.088346871  0.30067882 1.436204e-07
## 1M+-50K+     0.13258593  0.022293168  0.24287869 4.880458e-03

As we can see, the significant difference for average rating for different review categories is between 0+ and 1M+ as expected.

For easier Ratings and Reviews vs Installs we can group Installs into categories given

ANOVA test for Content Rating vs Installs

# 1. Encode content rating (e.g., as factor levels or one-hot encoding)
data_updated$Content.Rating <- as.factor(data_updated$Content.Rating)

data_updated <- data_updated %>%
  filter(!is.na(Installs) & Installs > 0)

# ANOVA test for difference in installs between content ratings
install_anova <- aov(log10(Installs) ~ Content.Rating, data = data_updated)

print("\nANOVA test results for Installs by Content Rating:")

## [1] "\nANOVA test results for Installs by Content Rating:"

print(summary(install_anova))

##                  Df Sum Sq Mean Sq F value Pr(>F)    
## Content.Rating    5    743  148.68   41.95 <2e-16 ***
## Residuals      9638  34160    3.54                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

ANOVA analysis : Revealed significant differences in install counts based on content rating (F(5, 9638) = 41.95, p < 2e-16). This indicates that various content ratings have a substantial impact on the number of installs, highlighting the importance of content quality and type in attracting users.

Data Preprocessing for Modelling

# Convert the 'last_updated' column to Date type
data_updated$last_updated <- as.Date(data_updated$last_updated, format = "%B %d, %Y")

# Calculate the difference in days between the maximum date and each date in 'last_updated'
data_updated$lastupdate <- as.numeric(difftime(max(data_updated$last_updated, na.rm = TRUE), 
                                       data_updated$last_updated, 
                                       units = "days"))


data_updated$last_updated <- NULL

Last updated

data_updated <- data_updated[, !(names(data_updated) %in% c(
  "Last.Updated", "Android.Ver", "last_updated", "current_date", 
  "days_since_update", "update_month", "season", 
  "Days.Since.Last.Update", "Content.Rating.Encoded","App"))]

# Rename a column
names(data_updated)[names(data_updated) == "Content.Rating"] <- "content_rating"
data_updated$content_rating <- as.numeric(data_updated$content_rating)

str(data_updated)

## 'data.frame':    9644 obs. of  8 variables:
##  $ Category      : Factor w/ 33 levels "ART_AND_DESIGN",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Rating        : num  4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
##  $ Reviews       : num  159 967 87510 215644 967 ...
##  $ Size          : num  19 14 8.7 25 2.8 5.6 19 29 33 3.1 ...
##  $ Installs      : num  1e+04 5e+05 5e+06 5e+07 1e+05 5e+04 5e+04 1e+06 1e+06 1e+04 ...
##  $ Price         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ content_rating: num  2 2 2 5 2 2 2 2 2 2 ...
##  $ lastupdate    : num  213 205 7 61 49 500 104 55 322 36 ...

Installs

# Load necessary libraries
library(ggplot2)

# Create two categories: Low Installs and High Installs
# Calculate the median of Installs to split into two categories
median_installs <- median(data_updated$Installs, na.rm = TRUE)


# Reclassify into two categories
data_updated$Installs_Category <- ifelse(data_updated$Installs <= median_installs, "Low Installs", "High Installs")


# Convert 'Installs_Category' to factor with levels "Low Installs" and "High Installs"
data_updated$Installs_Category <- factor(data_updated$Installs_Category, 
                                         levels = c("Low Installs", "High Installs"), 
                                         labels = c(0, 1))

# Check the conversion
table(data_updated$Installs_Category)

## 
##    0    1 
## 5744 3900

# Create a histogram for the new categories
ggplot(data_updated, aes(x = Installs_Category)) +
  geom_bar(stat = "count", fill = "skyblue", color = "black") +
  labs(title = "Histogram of Installs Category (Low vs High)",
       x = "Installs Category",
       y = "Count") +
  theme_minimal()

str(data_updated)

## 'data.frame':    9644 obs. of  41 variables:
##  $ Rating                : num  4.1 3.9 4.7 4.5 4.3 4.4 3.8 4.1 4.4 4.7 ...
##  $ Reviews               : num  159 967 87510 215644 967 ...
##  $ Size                  : num  19 14 8.7 25 2.8 5.6 19 29 33 3.1 ...
##  $ Installs              : num  1e+04 5e+05 5e+06 5e+07 1e+05 5e+04 5e+04 1e+06 1e+06 1e+04 ...
##  $ Price                 : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ content_rating        : num  2 2 2 5 2 2 2 2 2 2 ...
##  $ lastupdate            : num  213 205 7 61 49 500 104 55 322 36 ...
##  $ catART_AND_DESIGN     : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ catAUTO_AND_VEHICLES  : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catBEAUTY             : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catBOOKS_AND_REFERENCE: num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catBUSINESS           : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catCOMICS             : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catCOMMUNICATION      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catDATING             : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catEDUCATION          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catENTERTAINMENT      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catEVENTS             : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catFAMILY             : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catFINANCE            : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catFOOD_AND_DRINK     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catGAME               : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catHEALTH_AND_FITNESS : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catHOUSE_AND_HOME     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catLIBRARIES_AND_DEMO : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catLIFESTYLE          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catMAPS_AND_NAVIGATION: num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catMEDICAL            : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catNEWS_AND_MAGAZINES : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catPARENTING          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catPERSONALIZATION    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catPHOTOGRAPHY        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catPRODUCTIVITY       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catSHOPPING           : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catSOCIAL             : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catSPORTS             : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catTOOLS              : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catTRAVEL_AND_LOCAL   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catVIDEO_PLAYERS      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ catWEATHER            : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ Installs_Category     : Factor w/ 2 levels "0","1": 1 2 2 2 1 1 1 2 2 1 ...

So, now our final dataset after preprocessing is named as ‘data_updated’. We have selected five modelling techniques that would be best suitable for answering our SMART Question.

Data Modelling

Logistic Regression

KNN

#————————————————————————-

Gradient Boosting

# 2. Feature Engineering ------------------------------------------------

# Create binary target variable: High success (Installs >= median) vs. Low success
median_installs <- median(data_updated$Installs, na.rm = TRUE)
data_updated$Success <- ifelse(data_updated$Installs >= median_installs, 1, 0)

# Convert 'Category' to dummy variables (one-hot encoding)
# data_updated <- data_updated %>%
#   mutate(Category = as.factor(Category)) %>%
#   cbind(model.matrix(~ Category - 1, data_updated))


# Drop unused columns
#data_updated <- data_updated %>% select(-Android.Ver, -Content.Rating, -Last.Updated)

# 3. Data Splitting -----------------------------------------------------

# Separate features (X) and target (y)
#X <- data_final %>% select(-Installs, -Success)
X <- data_updated %>% select(-Installs, -Success)  # Exclude the target variable
y <- data_updated$Success             # Extract the target variable

table(y)

## y
##    0    1 
## 4632 5012

#Split into training and testing sets
set.seed(123)
train_index <- createDataPartition(y, p = 0.7, list = FALSE)

# Define X_train, X_test, y_train, y_test
X_train <- X[train_index, ] %>% mutate(across(everything(), as.numeric))
X_test <- X[-train_index, ] %>% mutate(across(everything(), as.numeric))
y_train <- y[train_index]
y_test <- y[-train_index]

# Check the structure

# Convert data to matrix for XGBoost
dtrain <- xgb.DMatrix(data = as.matrix(X_train), label = y_train)
dtest <- xgb.DMatrix(data = as.matrix(X_test), label = y_test)

# 4. Train Gradient Boosting Model --------------------------------------

params <- list(
  objective = "binary:logistic",  # Binary classification
  eval_metric = "logloss",
  max_depth = 6,
  eta = 0.1,
  subsample = 0.8,
  colsample_bytree = 0.8
)

# Train the model
set.seed(42)
xgb_model <- xgb.train(
  params = params,
  data = dtrain,
  nrounds = 100,
  watchlist = list(train = dtrain, test = dtest),
  early_stopping_rounds = 10,
  verbose = 1
)

## [1]  train-logloss:0.609961  test-logloss:0.610901 
## Multiple eval metrics are present. Will use test_logloss for early stopping.
## Will train until test_logloss hasn't improved in 10 rounds.
## 
## [2]  train-logloss:0.542076  test-logloss:0.544289 
## [3]  train-logloss:0.485184  test-logloss:0.488545 
## [4]  train-logloss:0.436916  test-logloss:0.441353 
## [5]  train-logloss:0.405047  test-logloss:0.409678 
## [6]  train-logloss:0.368169  test-logloss:0.373890 
## [7]  train-logloss:0.344374  test-logloss:0.350321 
## [8]  train-logloss:0.315461  test-logloss:0.323088 
## [9]  train-logloss:0.297176  test-logloss:0.304968 
## [10] train-logloss:0.273833  test-logloss:0.282865 
## [11] train-logloss:0.252967  test-logloss:0.263302 
## [12] train-logloss:0.234827  test-logloss:0.245916 
## [13] train-logloss:0.218308  test-logloss:0.231012 
## [14] train-logloss:0.203750  test-logloss:0.217901 
## [15] train-logloss:0.191103  test-logloss:0.206367 
## [16] train-logloss:0.179810  test-logloss:0.195884 
## [17] train-logloss:0.169687  test-logloss:0.186802 
## [18] train-logloss:0.163163  test-logloss:0.180602 
## [19] train-logloss:0.154717  test-logloss:0.172802 
## [20] train-logloss:0.150294  test-logloss:0.168185 
## [21] train-logloss:0.143017  test-logloss:0.161509 
## [22] train-logloss:0.136578  test-logloss:0.156003 
## [23] train-logloss:0.130758  test-logloss:0.150831 
## [24] train-logloss:0.125531  test-logloss:0.146289 
## [25] train-logloss:0.120791  test-logloss:0.142693 
## [26] train-logloss:0.118051  test-logloss:0.139899 
## [27] train-logloss:0.113890  test-logloss:0.136532 
## [28] train-logloss:0.110343  test-logloss:0.133553 
## [29] train-logloss:0.107051  test-logloss:0.130759 
## [30] train-logloss:0.104148  test-logloss:0.128428 
## [31] train-logloss:0.101383  test-logloss:0.126409 
## [32] train-logloss:0.099031  test-logloss:0.124483 
## [33] train-logloss:0.097492  test-logloss:0.123358 
## [34] train-logloss:0.095159  test-logloss:0.121917 
## [35] train-logloss:0.094473  test-logloss:0.121547 
## [36] train-logloss:0.092616  test-logloss:0.120371 
## [37] train-logloss:0.090554  test-logloss:0.119301 
## [38] train-logloss:0.088817  test-logloss:0.118244 
## [39] train-logloss:0.087445  test-logloss:0.117405 
## [40] train-logloss:0.086014  test-logloss:0.116530 
## [41] train-logloss:0.084754  test-logloss:0.115580 
## [42] train-logloss:0.083631  test-logloss:0.114904 
## [43] train-logloss:0.082379  test-logloss:0.114289 
## [44] train-logloss:0.081223  test-logloss:0.113716 
## [45] train-logloss:0.080282  test-logloss:0.113092 
## [46] train-logloss:0.079407  test-logloss:0.112634 
## [47] train-logloss:0.078634  test-logloss:0.112557 
## [48] train-logloss:0.077629  test-logloss:0.112271 
## [49] train-logloss:0.076872  test-logloss:0.112110 
## [50] train-logloss:0.076057  test-logloss:0.111826 
## [51] train-logloss:0.075172  test-logloss:0.111830 
## [52] train-logloss:0.074348  test-logloss:0.111555 
## [53] train-logloss:0.073854  test-logloss:0.111476 
## [54] train-logloss:0.073207  test-logloss:0.111389 
## [55] train-logloss:0.072740  test-logloss:0.111280 
## [56] train-logloss:0.071966  test-logloss:0.111071 
## [57] train-logloss:0.071495  test-logloss:0.110951 
## [58] train-logloss:0.070955  test-logloss:0.110909 
## [59] train-logloss:0.070698  test-logloss:0.110868 
## [60] train-logloss:0.070346  test-logloss:0.110629 
## [61] train-logloss:0.070082  test-logloss:0.110552 
## [62] train-logloss:0.069467  test-logloss:0.110589 
## [63] train-logloss:0.069110  test-logloss:0.110605 
## [64] train-logloss:0.068891  test-logloss:0.110235 
## [65] train-logloss:0.068188  test-logloss:0.109897 
## [66] train-logloss:0.067574  test-logloss:0.110155 
## [67] train-logloss:0.067239  test-logloss:0.110109 
## [68] train-logloss:0.067093  test-logloss:0.109921 
## [69] train-logloss:0.066588  test-logloss:0.110098 
## [70] train-logloss:0.066082  test-logloss:0.110419 
## [71] train-logloss:0.065747  test-logloss:0.110524 
## [72] train-logloss:0.065411  test-logloss:0.110210 
## [73] train-logloss:0.065194  test-logloss:0.110247 
## [74] train-logloss:0.064744  test-logloss:0.110331 
## [75] train-logloss:0.064438  test-logloss:0.110252 
## Stopping. Best iteration:
## [65] train-logloss:0.068188  test-logloss:0.109897

# 5. Model Evaluation ---------------------------------------------------

# Make predictions
y_pred <- predict(xgb_model, dtest)
y_pred_class <- ifelse(y_pred > 0.5, 1, 0)

# Confusion Matrix
y_pred_class <- factor(y_pred_class, levels = c(0, 1))
y_test <- factor(y_test, levels = c(0, 1))
conf_matrix <- confusionMatrix(y_pred_class, y_test)
print(conf_matrix)

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction    0    1
##          0 1331   66
##          1   61 1435
##                                          
##                Accuracy : 0.9561         
##                  95% CI : (0.948, 0.9633)
##     No Information Rate : 0.5188         
##     P-Value [Acc > NIR] : <2e-16         
##                                          
##                   Kappa : 0.9121         
##                                          
##  Mcnemar's Test P-Value : 0.7226         
##                                          
##             Sensitivity : 0.9562         
##             Specificity : 0.9560         
##          Pos Pred Value : 0.9528         
##          Neg Pred Value : 0.9592         
##              Prevalence : 0.4812         
##          Detection Rate : 0.4601         
##    Detection Prevalence : 0.4829         
##       Balanced Accuracy : 0.9561         
##                                          
##        'Positive' Class : 0              
##

# AUC and ROC Curve
roc_obj <- roc(as.numeric(as.character(y_test)), y_pred)
auc_value <- auc(roc_obj)
cat("AUC:", auc_value, "\n")

## AUC: 0.9914303

# Plot ROC Curve
plot(roc_obj, main = "ROC Curve", col = "blue", lwd = 2)
abline(a = 0, b = 1, lty = 2, col = "red")

# 6. Feature Importance -------------------------------------------------

importance_matrix <- xgb.importance(feature_names = colnames(X_train), model = xgb_model)
xgb.plot.importance(importance_matrix, top_n = 10, main = "Feature Importance")

# 7. Save Model ---------------------------------------------------------

xgb.save(xgb_model, "xgb_app_success.model")

## [1] TRUE

# Summary
cat("Gradient Boosting achieved an accuracy of", conf_matrix$overall["Accuracy"], 
    "and AUC of", auc_value, "\n")

## Gradient Boosting achieved an accuracy of 0.9561009 and AUC of 0.9914303

Decision Tree

# Remove the Installs and Installs numerical columns
data <- data_updated[, !colnames(data_updated) %in% c("Installs")]

# Split the data into training and testing sets
set.seed(123)  # Ensure reproducibility
trainIndex <- createDataPartition(data$Installs_Category, p = 0.8, list = FALSE)
trainData <- data[trainIndex, ]
testData <- data[-trainIndex, ]

# Load necessary libraries
library(rpart)
library(rpart.plot)

# Fit the decision tree model
set.seed(42)
tree_model <- rpart(
  Installs_Category ~ . ,
  data = trainData,
  method = "class"
)

# Plot the decision tree
rpart.plot(tree_model, main = "Decision Tree for Predicting Installs Category")

# Predict on training and test datasets
train_predictions <- predict(tree_model, trainData, type = "class")
test_predictions <- predict(tree_model, testData, type = "class")

# Calculate accuracy
train_accuracy <- sum(train_predictions == trainData$Installs_Category) / nrow(trainData)
test_accuracy <- sum(test_predictions == testData$Installs_Category) / nrow(testData)

# Print accuracy results
cat("Training Accuracy: ", train_accuracy, "\n")

## Training Accuracy:  0.9479005

cat("Test Accuracy: ", test_accuracy, "\n")

## Test Accuracy:  0.9470954

# Check feature importance
importance <- tree_model$variable.importance

# Print feature importance
cat("Feature Importance:\n")

## Feature Importance:

print(importance)

##    Reviews    Success lastupdate     Rating       Size    catGAME      Price 
## 2874.68475 1939.83505  537.88084  303.44420  276.97554  239.16318   85.62621

# Visualize feature importance (optional)
barplot(
  importance,
  main = "Feature Importance",
  xlab = "Features",
  ylab = "Importance",
  col = "steelblue",
  las = 2
)

Why shift to Random Forest? High Dimensionality: With 41 variables, random forest handles many features better and can identify the most important ones. Feature Importance: Random forest provides a ranking of feature importance, helping us understand which variables influence the Installs_Category. Accuracy: Random forest generally has better predictive accuracy for larger and more complex datasets.

Random Forest

In this analysis, we employ a Random Forest model to predict the number of installs based on the top 5 app categories. The Random Forest algorithm is a robust ensemble learning method that builds multiple decision trees and combines their predictions to improve accuracy and reduce overfitting.

The analysis will focus on:

Feature Selection: Using the top 5 categories as predictors, which are highly correlated with app performance metrics such as installs, ratings, and reviews. Model Objective: Accurately predict the number of installs by capturing the complex and nonlinear relationships between features using a Random Forest model. Evaluation Metrics: Assess the model’s performance using metrics such as Mean Squared Error (MSE), R-squared, and visualization of feature importance to ensure the model’s predictions are interpretable and actionable.

Splitting the data into train and test split

Building Random FOrest Classifier

library(randomForest)
library(caret)

# Train the random forest model
set.seed(123)
rf_model <- randomForest(Installs_Category ~ ., 
                         data = trainData, 
                         ntree = 500,       # Number of trees
                         mtry = 10,  # Number of predictors sampled at each split
                         importance = TRUE) # Enable importance calculation

# Print the model summary
print(rf_model)

## 
## Call:
##  randomForest(formula = Installs_Category ~ ., data = trainData,      ntree = 500, mtry = 10, importance = TRUE) 
##                Type of random forest: classification
##                      Number of trees: 500
## No. of variables tried at each split: 10
## 
##         OOB estimate of  error rate: 4.77%
## Confusion matrix:
##      0    1 class.error
## 0 4418  178  0.03872933
## 1  190 2930  0.06089744

Plotting the Random Forest Result

plot(rf_model)

Testing the accuracy

# Predictions on the training set
train_predictions <- predict(rf_model, trainData)

# Predictions on the testing set
test_predictions <- predict(rf_model, testData)

# Confusion Matrix for Training Data
train_cm <- confusionMatrix(train_predictions, trainData$Installs_Category)
print(train_cm)

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction    0    1
##          0 4585    2
##          1   11 3118
##                                           
##                Accuracy : 0.9983          
##                  95% CI : (0.9971, 0.9991)
##     No Information Rate : 0.5956          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9965          
##                                           
##  Mcnemar's Test P-Value : 0.0265          
##                                           
##             Sensitivity : 0.9976          
##             Specificity : 0.9994          
##          Pos Pred Value : 0.9996          
##          Neg Pred Value : 0.9965          
##              Prevalence : 0.5956          
##          Detection Rate : 0.5942          
##    Detection Prevalence : 0.5945          
##       Balanced Accuracy : 0.9985          
##                                           
##        'Positive' Class : 0               
##

# Confusion Matrix for Testing Data
test_cm <- confusionMatrix(test_predictions, testData$Installs_Category)
print(test_cm)

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction    0    1
##          0 1110   51
##          1   38  729
##                                           
##                Accuracy : 0.9538          
##                  95% CI : (0.9435, 0.9628)
##     No Information Rate : 0.5954          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.9039          
##                                           
##  Mcnemar's Test P-Value : 0.2034          
##                                           
##             Sensitivity : 0.9669          
##             Specificity : 0.9346          
##          Pos Pred Value : 0.9561          
##          Neg Pred Value : 0.9505          
##              Prevalence : 0.5954          
##          Detection Rate : 0.5757          
##    Detection Prevalence : 0.6022          
##       Balanced Accuracy : 0.9508          
##                                           
##        'Positive' Class : 0               
##

PLotting the Training, Testing and error Rate

# Calculate training and testing accuracy
train_accuracy <- sum(train_predictions == trainData$Installs_Category) / nrow(trainData)
test_accuracy <- sum(test_predictions == testData$Install_Category) / nrow(testData)

# Prepare data for plotting
errors <- data.frame(
  Trees = 1:500,
  OOB = rf_model$err.rate[, 1],
  TrainAccuracy = rep(train_accuracy, 500),
  TestAccuracy = rep(test_accuracy, 500)
)

# Plot the errors
plot(errors$Trees, errors$OOB, type = "l", col = "blue", lwd = 2, 
     ylim = c(0, 1), xlab = "Number of Trees", ylab = "Error/Accuracy", 
     main = "Training, Testing, and OOB Error Rates")
lines(errors$Trees, 1 - errors$TrainAccuracy, col = "green", lwd = 2, lty = 2)  # Training error
lines(errors$Trees, 1 - errors$TestAccuracy, col = "red", lwd = 2, lty = 2)  # Testing error
legend("topright", legend = c("OOB Error", "Training Error", "Testing Error"), 
       col = c("blue", "green", "red"), lwd = 2, lty = c(1, 2, 2))

Feature Importance Values

# Variable importance
importance(rf_model)

##                                 0           1 MeanDecreaseAccuracy
## Rating                 12.3143267  16.6806973          19.98208469
## Reviews                29.1350290 151.8967955          77.30996319
## Size                    6.6652651   9.7777886          12.02398142
## Price                  46.9206523  57.2074894          64.43385934
## content_rating         10.2770840   1.5923851           8.21855263
## lastupdate              6.0252421  13.7498776          14.81094638
## catART_AND_DESIGN      -4.6051233  -0.2165312          -3.45042719
## catAUTO_AND_VEHICLES   -3.5269954   3.9297943           0.79451583
## catBEAUTY              -1.8818210   0.7175801          -0.83122085
## catBOOKS_AND_REFERENCE  1.2987280   6.1796501           5.68559158
## catBUSINESS             1.3008149   4.2574561           4.61281955
## catCOMICS               2.5698036  -2.2443011           0.05459819
## catCOMMUNICATION        0.6471630  -0.4981754           0.06433939
## catDATING               0.1662498  -3.9717151          -3.24500605
## catEDUCATION            0.4332665   5.2348424           3.13959574
## catENTERTAINMENT        4.3583673   0.0749067           4.27366696
## catEVENTS               7.3509912   9.4533758          11.26804079
## catFAMILY              -4.2677780   4.8104059           2.37953616
## catFINANCE              2.7344530   0.9457580           2.65559850
## catFOOD_AND_DRINK      -4.3344813   0.1210452          -2.92672629
## catGAME                 5.2343298  -2.3799702           2.87150712
## catHEALTH_AND_FITNESS  -2.7017337   2.5330758           0.01158012
## catHOUSE_AND_HOME      -1.8879437  -0.8949606          -1.89664750
## catLIBRARIES_AND_DEMO  -5.1229906   2.7183140          -2.39524501
## catLIFESTYLE            2.8988634  -2.5279339           0.19344419
## catMAPS_AND_NAVIGATION -1.3880427  -1.3726584          -1.89691932
## catMEDICAL             -1.7768118  14.9552421          14.41703332
## catNEWS_AND_MAGAZINES   2.7310481   4.3920915           5.32529587
## catPARENTING           -5.3327553   4.0424688          -0.92325506
## catPERSONALIZATION     -0.7005746   2.9871164           1.66920698
## catPHOTOGRAPHY          5.1940963   2.4619638           5.65700702
## catPRODUCTIVITY         3.0805891  -3.0220535           0.11810667
## catSHOPPING            -0.7297752  -1.9448289          -1.85870472
## catSOCIAL              -1.7231937  -0.8326130          -1.85841365
## catSPORTS               0.9142572  -1.1389045          -0.28319962
## catTOOLS                3.5425592   3.7780611           5.26140007
## catTRAVEL_AND_LOCAL     1.1628032   0.2542499           1.10365293
## catVIDEO_PLAYERS        1.0405432  -0.2697573           0.49874408
## catWEATHER             -2.7008219  -3.4120292          -4.29987447
## Success                24.5198424  36.2257913          42.33695561
##                        MeanDecreaseGini
## Rating                      132.4771859
## Reviews                    1990.4086063
## Size                        153.4647688
## Price                        82.7671081
## content_rating               21.1893183
## lastupdate                  179.7881521
## catART_AND_DESIGN             1.9802876
## catAUTO_AND_VEHICLES          3.8894313
## catBEAUTY                     1.2238681
## catBOOKS_AND_REFERENCE        6.0756462
## catBUSINESS                   4.2035011
## catCOMICS                     2.2112898
## catCOMMUNICATION              0.9468554
## catDATING                     2.8769271
## catEDUCATION                  6.1706757
## catENTERTAINMENT              3.0470463
## catEVENTS                     2.8896901
## catFAMILY                    11.7899708
## catFINANCE                    4.0657885
## catFOOD_AND_DRINK             2.4636973
## catGAME                      13.8727408
## catHEALTH_AND_FITNESS         5.5084624
## catHOUSE_AND_HOME             3.0931222
## catLIBRARIES_AND_DEMO         1.8163560
## catLIFESTYLE                  5.1587322
## catMAPS_AND_NAVIGATION        1.2967987
## catMEDICAL                   12.6007062
## catNEWS_AND_MAGAZINES         3.0533446
## catPARENTING                  3.0709891
## catPERSONALIZATION            3.9217580
## catPHOTOGRAPHY                6.9459726
## catPRODUCTIVITY               4.7614276
## catSHOPPING                   2.3981296
## catSOCIAL                     3.3124995
## catSPORTS                     5.3446551
## catTOOLS                      7.5616495
## catTRAVEL_AND_LOCAL           3.1566718
## catVIDEO_PLAYERS              3.1459915
## catWEATHER                    2.8293681
## Success                     919.6286663

# Plot variable importance
varImpPlot(rf_model)

#### Visualization for Feature Importance

# Extract importance values
importance_values <- importance(rf_model)
importance_df <- data.frame(
  Feature = rownames(importance_values),
  MeanDecreaseAccuracy = importance_values[, "MeanDecreaseAccuracy"],
  MeanDecreaseGini = importance_values[, "MeanDecreaseGini"]
)

# Plot Mean Decrease in Accuracy
accuracy_plot <- ggplot(importance_df, aes(x = reorder(Feature, MeanDecreaseAccuracy), y = MeanDecreaseAccuracy)) +
  geom_bar(stat = "identity", fill = "skyblue") +
  coord_flip() +
  labs(
    title = "Feature Importance (Mean Decrease in Accuracy)",
    x = "Features",
    y = "Importance"
  ) +
  theme_minimal() +
  theme(text = element_text(size = 12), axis.text.y = element_text(size = 10))

# Plot the accuracy plot
print(accuracy_plot)

# Save the plot with larger dimensions
#ggsave("feature_importance_accuracy_large.png", plot = accuracy_plot, width = 12, height = 10, dpi = 300)

# Plot Mean Decrease in Gini
gini_plot <- ggplot(importance_df, aes(x = reorder(Feature, MeanDecreaseGini), y = MeanDecreaseGini)) +
  geom_bar(stat = "identity", fill = "lightgreen") +
  coord_flip() +
  labs(
    title = "Feature Importance (Mean Decrease in Gini)",
    x = "Features",
    y = "Importance"
  ) +
  theme_minimal() +
  theme(text = element_text(size = 12), axis.text.y = element_text(size = 10))

# Plot the gini index plot
print(gini_plot)

# Save the plot with larger dimensions
#ggsave("feature_importance_gini_large.png", plot = gini_plot, width = 12, height = 10, dpi = 300)

SVM

# Convert target variable to a factor
y <- as.factor(data_updated$Installs_Category)

# Remove unused columns
X <- data_updated[, !names(data_updated) %in% c('Installs', 'Installs_Category', 'Installs_Num')]

# Split data into training and testing sets
set.seed(42)
trainIndex <- createDataPartition(y, p = 0.75, list = FALSE)
X_train <- X[trainIndex, ]
X_test <- X[-trainIndex, ]
y_train <- y[trainIndex]
y_test <- y[-trainIndex]

Checking if the boundary is non-linear or linear

library(plotly)

# Prepare the plot data
plot_data <- data.frame(X_train, Class = as.factor(y_train))

# Create 3D scatter plot for features 1, 2, and 3
plot_1_2_3 <- plot_ly(data = plot_data, 
                      x = ~X_train[, 1], 
                      y = ~X_train[, 2], 
                      z = ~X_train[, 3], 
                      color = ~Class,
                      colors = c("red", "blue"),  # Set colors for classes (0 and 1)
                      type = 'scatter3d', 
                      mode = 'markers') %>%
  layout(title = "3D Scatter Plot: Feature 1 vs Feature 2 vs Feature 3",
         scene = list(xaxis = list(title = colnames(X_train)[1]),
                      yaxis = list(title = colnames(X_train)[2]),
                      zaxis = list(title = colnames(X_train)[3])))

# Create 3D scatter plot for features 4, 5, and 6
plot_4_5_6 <- plot_ly(data = plot_data, 
                      x = ~X_train[, 4], 
                      y = ~X_train[, 5], 
                      z = ~X_train[, 6], 
                      color = ~Class,
                      colors = c("red", "blue"),  # Set colors for classes (0 and 1)
                      type = 'scatter3d', 
                      mode = 'markers') %>%
  layout(title = "3D Scatter Plot: Feature 4 vs Feature 5 vs Feature 6",
         scene = list(xaxis = list(title = colnames(X_train)[4]),
                      yaxis = list(title = colnames(X_train)[5]),
                      zaxis = list(title = colnames(X_train)[6])))

# Show plots
plot_1_2_3

plot_4_5_6

As we can see we cannot decide if the boundary is linear or non-linear hence, lets make two models linear and non-linear SVM to check which one is a better fit.

Tuning to find the best parameter values for C and Gamma for SVM non-linear

# Load necessary libraries
library(e1071)
library(caret)

# Assuming you have already defined X_train, y_train, X_test, y_test

# Combine the training data into a data frame
train_data <- as.data.frame(cbind(X_train, y_train))

# Set up k-fold cross-validation
set.seed(42)
train_control <- trainControl(method = "cv", number = 5)

# Define the tuning grid for 'C' and 'sigma' (gamma)
tune_grid <- expand.grid(C = c( 0.1, 1, 10, 100),
                          sigma = c(0.5, 1))

# Train the SVM model using radial kernel with the tuning grid
svm_model <- train(y_train ~ ., data = train_data,
                   method = "svmRadial",
                   tuneGrid = tune_grid,
                   trControl = train_control,scaled = TRUE)

# Print the results of the tuning
print(svm_model)

## Support Vector Machines with Radial Basis Function Kernel 
## 
## 7233 samples
##   40 predictor
##    2 classes: '0', '1' 
## 
## No pre-processing
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 5786, 5787, 5786, 5787, 5786 
## Resampling results across tuning parameters:
## 
##   C      sigma  Accuracy   Kappa    
##     0.1  0.5    0.8472313  0.6830532
##     0.1  1.0    0.8110081  0.5974862
##     1.0  0.5    0.8758494  0.7486983
##     1.0  1.0    0.8678302  0.7306049
##    10.0  0.5    0.8721155  0.7392163
##    10.0  1.0    0.8623001  0.7164166
##   100.0  0.5    0.8642357  0.7199747
##   100.0  1.0    0.8533130  0.6950222
## 
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.5 and C = 1.

# Best model parameters
best_params <- svm_model$bestTune
cat("Best Parameters:\n")

## Best Parameters:

print(best_params)

##   sigma C
## 3   0.5 1

As seen for the training set the best accuracy is achieved when C = 100 and gamma is 0.05

Tuning to find the best parameter values for C for SVM linear

# Load necessary libraries
library(e1071)
library(caret)

# Assuming you have already defined X_train, y_train, X_test, y_test

# Combine the training data into a data frame
train_data <- as.data.frame(cbind(X_train, y_train))

# Set up k-fold cross-validation
set.seed(42)
train_control <- trainControl(method = "cv", number = 5)

# Define the tuning grid for 'C' and 'sigma' (gamma)
tune_grid <- expand.grid(C = c( 0.1, 1, 10, 100))

# Train the SVM model using radial kernel with the tuning grid
svm_model <- train(y_train ~ ., data = train_data,
                   method = "svmLinear",
                   tuneGrid = tune_grid,
                   trControl = train_control,scaled = TRUE)

# Print the results of the tuning
print(svm_model)

## Support Vector Machines with Linear Kernel 
## 
## 7233 samples
##   40 predictor
##    2 classes: '0', '1' 
## 
## No pre-processing
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 5786, 5787, 5786, 5787, 5786 
## Resampling results across tuning parameters:
## 
##   C      Accuracy   Kappa    
##     0.1  0.8842829  0.7692133
##     1.0  0.8903658  0.7805455
##    10.0  0.8960346  0.7914651
##   100.0  0.9300447  0.8539968
## 
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was C = 100.

# Best model parameters
best_params <- svm_model$bestTune
cat("Best Parameters:\n")

## Best Parameters:

print(best_params)

##     C
## 4 100

For linear model it could be seen that at C = 100, we attain an accuracy of 92 percent which suggets that the model is linearly seperable. Hence, now lets find the accuracy, ROC, AUC score of the test data.

Bias variance MSE caluclation

# Load necessary libraries
library(e1071)
library(ggplot2)
library(reshape2)

# Assuming you have already defined X_train, y_train, X_test, y_test

# Combine training and test sets into data frames
train_data <- as.data.frame(cbind(X_train, y_train))
test_data <- as.data.frame(X_test)

# Initialize vectors to store metrics
C_values <- c(0.1, 1, 10, 100, 1000, 100000, 500000)
bias_values <- c()
variance_values <- c()
mse_values <- c()

# Loop through different values of C
for (C in C_values) {
  # Train the SVM model
  svm_model <- svm(y_train ~ ., data = train_data, kernel = "linear", cost = C, scale = TRUE)
  
  # Predict on the test set
  predictions <- predict(svm_model, newdata = test_data)
  
  # Convert predictions and actual test values to numeric for calculations
  predictions_numeric <- as.numeric(as.character(predictions))
  y_test_numeric <- as.numeric(as.character(y_test))
  
  # Calculate Bias: Mean squared difference between the true mean and the predicted mean
  mean_true <- mean(y_test_numeric)
  mean_pred <- mean(predictions_numeric)
  bias <- (mean_true - mean_pred)^2
  bias_values <- c(bias_values, bias)
  
  # Calculate Variance: Variability of the predictions
  variance <- var(predictions_numeric)
  variance_values <- c(variance_values, variance)
  
  # Calculate Mean Squared Error (MSE): Average squared error
  mse <- mean((y_test_numeric - predictions_numeric)^2)
  mse_values <- c(mse_values, mse)
}

# Combine results into a data frame for plotting
results <- data.frame(
  C = C_values,
  Bias = bias_values,
  Variance = variance_values,
  MSE = mse_values
)

# Reshape data for ggplot
results_melted <- melt(results, id.vars = "C", variable.name = "Metric", value.name = "Value")

# Plotting
ggplot(results_melted, aes(x = factor(C), y = Value, color = Metric, group = Metric)) +
  geom_line(size = 1) +
  geom_point(size = 3) +
  labs(title = "Bias-Variance Tradeoff and MSE for Different C Values",
       x = "C Values",
       y = "Value",
       color = "Metric") +
  theme_minimal(base_size = 15) +
  theme(
    plot.title = element_text(hjust = 0.5, face = "bold"),
    legend.position = "top"
  ) +
  scale_color_manual(values = c("Bias" = "blue", "Variance" = "red", "MSE" = "green")) +
  scale_x_discrete(labels = function(x) paste0("C = ", x)) +
  geom_text(aes(label = round(Value, 2)), vjust = -0.5)  # Add value labels above points

The analysis of bias, variance, and mean squared error (MSE) across different values of C shows: MSE Stability: From C = 100 to C = 1000, the MSE remains nearly constant. Low Bias and Variance: Both metrics are minimized in this range, indicating good generalization without overfitting.

Hence, better to select C = 100 for the SVM model to achieve an optimal balance between bias, variance, and MSE.

Model Evalution for Test Data

# Load necessary libraries
library(e1071)
library(pROC)  # For ROC and AUC

# Assuming you have already defined X_train, y_train, X_test, y_test

# Combine the training data into a data frame
train_data <- as.data.frame(cbind(X_train, y_train))

# Fit the SVM model with linear kernel
svm_model <- svm(y_train ~ ., data = train_data, kernel = "linear", cost = 100, decision.values = TRUE,scaled = TRUE)

# Step 1: Make predictions on the test set
predictions <- predict(svm_model, newdata = as.data.frame(X_test))

# Step 2: Create confusion matrix
confusion_matrix <- table(Predicted = predictions, Actual = y_test)
cat("Confusion Matrix:\n")

## Confusion Matrix:

print(confusion_matrix)

##          Actual
## Predicted    0    1
##         0 1389  101
##         1   47  874

confusion_matrix_caret <- confusionMatrix(confusion_matrix)

# Step 2: Extract Precision, Recall, and F1 Score
precision <- confusion_matrix_caret$byClass['Precision']
recall <- confusion_matrix_caret$byClass['Recall']
f1_score <- confusion_matrix_caret$byClass['F1']

# Display the metrics
cat("Precision:", precision, "\n")

## Precision: 0.9322148

cat("Recall:", recall, "\n")

## Recall: 0.9672702

cat("F1 Score:", f1_score, "\n")

## F1 Score: 0.949419

accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
cat("Accuracy:", accuracy, "\n")

## Accuracy: 0.9386147

# Step 4: Get decision values for ROC curve
fitted <- attributes(predict(svm_model, newdata = as.data.frame(X_test), decision.values = TRUE))$decision.values

# Step 5: Generate ROC plot for the test set
roc_curve <- roc(y_test, -fitted)  # Note: Use negative for class labeling

# Plot the ROC curve
plot(roc_curve, main = "ROC Curve for Test Data")
# Add AUC to the plot
auc_value <- auc(roc_curve)
legend("bottomright", legend = paste("AUC =", round(auc_value, 2)), bty = "n")

The model achieved an impressive accuracy of 94% and a high AUC of 0.99, indicating excellent performance in classification. Additionally, the high precision, recall, and F1-score values reflect the model’s capability to accurately classify both class 0 and class 1. These results suggest that the model is highly effective in distinguishing between the two classes, making it reliable for practical applications.

Feature importance for the model

# Assuming you have already trained your SVM model (svm_model) using e1071

# Get coefficients from the SVM model
coefficients <- as.vector(svm_model$coefs) %*% svm_model$SV

# Get the intercept term
intercept <- svm_model$rho

# Combine coefficients and intercept into a single vector
all_coefficients <- c(intercept, coefficients)

# Print coefficients
cat("Coefficients (including intercept):\n")

## Coefficients (including intercept):

print(all_coefficients)

##  [1]  2.085058e+01  1.087825e-01 -1.767870e+02 -1.390152e-01  1.299278e+01
##  [6]  8.604962e-02  9.476142e-02  3.703294e-02  4.115168e-02  4.284333e-05
## [11]  4.955957e-03  4.937072e-02  2.413850e-02 -3.674008e-02 -5.317158e-02
## [16] -7.816514e-02 -4.658387e-02  3.927985e-02  1.278253e-01  5.874558e-02
## [21]  3.353022e-02  3.754532e-02 -1.142397e-01 -7.283130e-02 -3.366334e-02
## [26]  1.347114e-02  3.575670e-03  1.449530e-01  6.237576e-02  1.882492e-02
## [31] -1.224170e-01 -1.774672e-01 -3.534363e-02 -7.687325e-02 -6.545884e-02
## [36]  5.654285e-02  5.225918e-02 -8.242140e-02 -1.134773e-01 -1.478470e-02
## [41] -9.333093e-01

# Check the number of coefficients
num_coefficients <- length(all_coefficients)
cat("Number of Coefficients (including intercept):", num_coefficients, "\n")

## Number of Coefficients (including intercept): 41

# Get feature names
feature_names <- colnames(X_train)

# Create a named vector for coefficients with feature names
named_coefficients <- setNames(coefficients, feature_names)

# Print named coefficients
cat("Feature Coefficients:\n")

## Feature Coefficients:

print(named_coefficients)

##         Rating  Reviews       Size    Price content_rating lastupdate
## [1,] 0.1087825 -176.787 -0.1390152 12.99278     0.08604962 0.09476142
##      catART_AND_DESIGN catAUTO_AND_VEHICLES    catBEAUTY catBOOKS_AND_REFERENCE
## [1,]        0.03703294           0.04115168 4.284333e-05            0.004955957
##      catBUSINESS catCOMICS catCOMMUNICATION   catDATING catEDUCATION
## [1,]  0.04937072 0.0241385      -0.03674008 -0.05317158  -0.07816514
##      catENTERTAINMENT  catEVENTS catFAMILY catFINANCE catFOOD_AND_DRINK
## [1,]      -0.04658387 0.03927985 0.1278253 0.05874558        0.03353022
##         catGAME catHEALTH_AND_FITNESS catHOUSE_AND_HOME catLIBRARIES_AND_DEMO
## [1,] 0.03754532            -0.1142397        -0.0728313           -0.03366334
##      catLIFESTYLE catMAPS_AND_NAVIGATION catMEDICAL catNEWS_AND_MAGAZINES
## [1,]   0.01347114             0.00357567   0.144953            0.06237576
##      catPARENTING catPERSONALIZATION catPHOTOGRAPHY catPRODUCTIVITY catSHOPPING
## [1,]   0.01882492          -0.122417     -0.1774672     -0.03534363 -0.07687325
##        catSOCIAL  catSPORTS   catTOOLS catTRAVEL_AND_LOCAL catVIDEO_PLAYERS
## [1,] -0.06545884 0.05654285 0.05225918          -0.0824214       -0.1134773
##      catWEATHER    Success
## [1,] -0.0147847 -0.9333093
## attr(,"names")
##  [1] "Rating"                 "Reviews"                "Size"                  
##  [4] "Price"                  "content_rating"         "lastupdate"            
##  [7] "catART_AND_DESIGN"      "catAUTO_AND_VEHICLES"   "catBEAUTY"             
## [10] "catBOOKS_AND_REFERENCE" "catBUSINESS"            "catCOMICS"             
## [13] "catCOMMUNICATION"       "catDATING"              "catEDUCATION"          
## [16] "catENTERTAINMENT"       "catEVENTS"              "catFAMILY"             
## [19] "catFINANCE"             "catFOOD_AND_DRINK"      "catGAME"               
## [22] "catHEALTH_AND_FITNESS"  "catHOUSE_AND_HOME"      "catLIBRARIES_AND_DEMO" 
## [25] "catLIFESTYLE"           "catMAPS_AND_NAVIGATION" "catMEDICAL"            
## [28] "catNEWS_AND_MAGAZINES"  "catPARENTING"           "catPERSONALIZATION"    
## [31] "catPHOTOGRAPHY"         "catPRODUCTIVITY"        "catSHOPPING"           
## [34] "catSOCIAL"              "catSPORTS"              "catTOOLS"              
## [37] "catTRAVEL_AND_LOCAL"    "catVIDEO_PLAYERS"       "catWEATHER"            
## [40] "Success"

# Sort coefficients by absolute value for feature importance
sorted_coefficients <- sort(abs(named_coefficients), decreasing = TRUE)

# Print sorted feature importance
cat("Sorted Feature Importance:\n")

## Sorted Feature Importance:

print(sorted_coefficients)

##                Reviews                  Price                Success 
##           1.767870e+02           1.299278e+01           9.333093e-01 
##         catPHOTOGRAPHY             catMEDICAL                   Size 
##           1.774672e-01           1.449530e-01           1.390152e-01 
##              catFAMILY     catPERSONALIZATION  catHEALTH_AND_FITNESS 
##           1.278253e-01           1.224170e-01           1.142397e-01 
##       catVIDEO_PLAYERS                 Rating             lastupdate 
##           1.134773e-01           1.087825e-01           9.476142e-02 
##         content_rating    catTRAVEL_AND_LOCAL           catEDUCATION 
##           8.604962e-02           8.242140e-02           7.816514e-02 
##            catSHOPPING      catHOUSE_AND_HOME              catSOCIAL 
##           7.687325e-02           7.283130e-02           6.545884e-02 
##  catNEWS_AND_MAGAZINES             catFINANCE              catSPORTS 
##           6.237576e-02           5.874558e-02           5.654285e-02 
##              catDATING               catTOOLS            catBUSINESS 
##           5.317158e-02           5.225918e-02           4.937072e-02 
##       catENTERTAINMENT   catAUTO_AND_VEHICLES              catEVENTS 
##           4.658387e-02           4.115168e-02           3.927985e-02 
##                catGAME      catART_AND_DESIGN       catCOMMUNICATION 
##           3.754532e-02           3.703294e-02           3.674008e-02 
##        catPRODUCTIVITY  catLIBRARIES_AND_DEMO      catFOOD_AND_DRINK 
##           3.534363e-02           3.366334e-02           3.353022e-02 
##              catCOMICS           catPARENTING             catWEATHER 
##           2.413850e-02           1.882492e-02           1.478470e-02 
##           catLIFESTYLE catBOOKS_AND_REFERENCE catMAPS_AND_NAVIGATION 
##           1.347114e-02           4.955957e-03           3.575670e-03 
##              catBEAUTY 
##           4.284333e-05

top_coef = head(sorted_coefficients,15)
# Optional: Visualize feature importance
barplot(
  top_coef,
  main = "Feature Importance from SVM Coefficients",
  xlab = "Features",
  col = "steelblue",
  las = 2,
  cex.names = 0.3,# Adjust name size if necessary
  horiz = TRUE
)

The analysis shows that Reviews, Price, and Size are the top features influencing the model’s performance. Additionally, the leading app categories—Photography, Medical, Family, Personalization, and Health and Fitness—suggest that focusing on these areas can enhance the chances of app success.

DATS 6101 - Final Project

Name: Snehitha Tadapaneni, Sai Rachana Kandikattu, Amrutha Jayachandradhara, Wilona Nguyen, Pramod Krishnachari

2024-12-07

Introduction

Smart Question

Overview

Data Preparation and Cleaning

Description of the App Dataset Columns

Apps

Checking for duplicated apps and removing

After dropping duplicate

Price

Convertion of Price to numerical

Checking for missing values in Price

Missing values removed succesfully. (Price)

Type

Missing values handles succesfully. (Type)

Size

Replacing Misiing values with the mean (Size)

Installs

Display the unique values

Rating and Reviews

Checking the format of Rating and Reviews

Change the column reviews from Str to int

Checking for Outliers For rating by seeing frequency for each rating

Category

Current Version & Genres

Content Rating, Last Updated

After cleaning the Data

Data Exploring and Visualization

Visualization for Price Distribution

Checking outliers for Price

Visualization for Type Distribution

Histogram for price distribution by App Type

Dropping the Type column

The Type column is successfully removed.

Visualization for Price vs Installs

Visualization for Price vs Reviews & Rating

Visualization for Price vs Reviews vs Installs

Visualization for Price vs Size

Visulization for Distribution of Installs

Visualization for Rating Distribution

Visualization for Reviews

Review frequency table

Binned reviews

Boxplots for Rating vs Reviews

Mean value of Ratings for each Review bins

Histogram for Reviews and Rating

Visualization for Reviews vs Installs

Visualization for Rating vs Installs

Visualization for Rating vs Installs by Category

Visualization for Category Distribution

Visualization for Category vs. Installs

Visualization for Category vs. App Size

Visualization for Category vs. Reviews

Histogram for Category vs. Rating

Visualization for Android Version

Bar plot for Android Version vs. Installs

Boxplot for Android Version vs. Reviews

Histogram for Android Version vs. Rating

Distribution for Content.Rating

Visualization for Content Rating

Observation for Update Frequency Velocity Analysis:

Visualization for Content Rating vs Installs

Distribution of Installations by Update Recency and Content Rating

Visualization for Last Updated vs Content Rating vs Installs

Correlation

Correlation for all variables in data_final

Correlation between time analysis variables VS Installs

Statistical Tests

Statistical test for Installs and Price

T-Test for Reviews and Price

ANOVA Test for Reviews vs Ratings

Post Hoc Test

ANOVA test for Content Rating vs Installs

Data Preprocessing for Modelling

Last updated

Category

Installs

Data Modelling